#rag

Earlier I posted in a “Who wants to be hired?” thread, looking for a place where I could apply my experience in hospitality, food tech and automation. A couple hours later I received an email: “Hi Ilia, I saw your comment on the June Who’s…

rag

The image is from X, been thinking about it since I saw it. Vibe coding is real.

rag

My father teaches geology and has literally zero coding expertise. Back in February, I introduced him to Claude and taught him the absolute basics of how Git works.

ragclaude-code

tool-callingrag

This question will probably make more sense when I explain my current situation: lately I’ve been doing some small projects here and there to some small business in my town and they have been working fine, but that is about to change. I ma…

ragclaude-code

Cross Canon Leave blank to search all indexed books. Loading scripture text...

rag

We’ve all seen the flashy demos, but after spending the last few months trying to build [or use] actual multi-agent workflows, I’ve hit a wall. The "Loop of Death": Agents still get stuck in reasoning loops that burn tokens without solving…

ragchatgpt

Just swapped Qwen 3.5 for the 3.6 variant (FP8, RTX 6000 Pro) using the same recommended generation settings. My stack is vLLM (v0.19.0) + Open WebUI (v0.8.12) in a RAG setup where the model has access to several document retrieval tools.

↯ Vllm↯ Qwen 3.6vllmragqwen

Gemini API File Search is now multimodal: build efficient, verifiable RAG Today, we are expanding the Gemini API’s File Search tool. You can now build retrieval-augmented generation (RAG) systems with multimodal data and custom metadata.

raggemini

I've seen so many rag this, memory that projects. What projects are people actually using day to day for agentic workloads.

ragmcpagentic

This is a project that I have been building for a while now, YourMemory is a solution to agentic memory which focuses on pruning of noise rather than hoarding of data. In the current state of agentic memory most of the context is stored in…

ragagentic

I benchmarked vision-capable LLMs (the "just attach the PDF and let the model read it" pattern) against OCR-based pipelines on 30 long, image-heavy PDFs from MMLongBench-Doc (https://github.com/mayubo2333/MMLongBench-Doc). There were 171 q…

↯ Sonnet 4.5ragsonnetagentic

The Open Source AI Framework for Production Ready Agents, RAG & Context Engineering Haystack Sets the Standard for Agentic AI Across Industries Why Teams Choose Haystack for their AI Workflows Build Transparent, Context Engineered AI Syste…

ragagentic

Previously a model could only be present in a single group. Now you can create whatever groups you want: one for big models that should run on their own, a group for STT + bigger model, a group for RAG usages, etc.

↯ Llamallamarag

keep seeing the same pattern across agent projects. we're good at building agents that find information, but the moment we ask them to actually do something (update a crm, trigger a payment, touch a production database), things grind to a…

ragagentic

rag

Hey HN! We built a browser-based agent that runs inside an authenticated web app, watches how the app calls its own APIs, and automatically turns those into agent tools.

ragmcp

Everybody here is posting their optimizations for running different models - thats good but make these benchmark realistic as speed is not one factor to run llm effectively. Context size is key - with agentic/coding/rag work you need to ha…

ragagentic

Been vibe coding a lot recently and kept running into the same problem finding actually usable tools without paying for 10 different subscriptions or donating my bank balance to Claude. So I put together a curated list focused on free or l…

↯ Ollama↯ Llamaollamallamarag+3

Contextual News Search APIs: A Deep Comparison for AI, RAG, and Research Last reviewed: August 21, 2026 Executive summary The market for "news search APIs" now contains several very different products under the same label. Some services se…

rag

I don't see any threads on this model. Is it because it's dense and/or without-reasoning?

↯ Qwen 3.6retrieval-augmentedrag

struggling with a bit of a reality check lately and wanted to see if anyone else is running into this. been pitching agentic workflows for a while, and I've realized that leading with the tech - the orchestration the RAG, the "intelligence…

ragagentic

I kept seeing inference-speed claims for these models and wanting an apples-to-apples comparison on the hardware I actually have. So I built a harness and a public page that dumps every run as YAML.

↯ Vllm↯ Gemma↯ Qwen 3.5vllmgemmamoe+1

Hey everyone! This is XTrace.

vector-databaserag

We had a customer support RAG bot. Standard setup: ChromaDB, system prompt, an LLM doing generation.

rag

So I've been going down a rabbit hole lately and I can't find many people actually talking about this specific use case. everyone here runs local LLMs for coding, chat, maybe some creative writing.

rag

Got an email today about the announcement. > OpenAI is winding down the fine-tuning API and platform.

↯ Fine Tuningfine-tuningragopenai

↯ Ollamaollamaopenclawrag+1

AI Engineer Notebooks Learn the applied-LLM stack the way you'll actually be interviewed on it — framework-free, on a free API, from prompting all the way to serving, fine-tuning, and a red-team benchmark. Runnable Colab notebooks for the…

↯ Fine Tuningfine-tuningrag

Qualcomm recently released the new 𝐒𝐧𝐚𝐩𝐝𝐫𝐚𝐠𝐨𝐧 𝐗2 𝐥𝐚𝐩𝐭𝐨𝐩 𝐜𝐡𝐢𝐩𝐬𝐞𝐭. I immediately ordered one: ASUS Zenbook A16 16" 3K OLED Touchscreen Laptop — Snapdragon X2 Elite Extreme (2026) A few things I really like about this machine: 𝐄𝐱𝐭𝐫𝐞𝐦𝐞𝐥𝐲 𝐥𝐢𝐠𝐡𝐭.

rag

I’m building some infrastructure around AI agents and I’m trying to understand how people are actually using them in real workflows, not demos. Specifically curious about: - What your agent actually does day-to-day (not hypotheticals) - Wh…

rag

I'm a Type 1 diabetic and software engineer. Last year I went months between endocrinologists with no clinician reviewing my data.

rag

rag

Upload markdown docs, get a support chatbot that answers with citations. The entire backend is one JS file — storage, search, and conversation history are handled by the runtime.

rag

Working on a multi-agent system for a large outbound pipeline. We're running 100+ LinkedIn and email accounts, and simple linear automation (step A then step B) breaks down fast because real conversations don't move in a straight line.

rag

I have been benchmarking local LLMs on a Mac M4 Pro 24 GB RAM using LM Studio. I've tested mostly with 4-bit quantization, both MLX and GGUF, from 4b to 35b models, with speeds of 3 to 40 tokens/second.

↯ Gemma↯ Qwen 3.6gemmaragqwen

Hi Hacker News, I'm Louis. I built Screenpipe (https://screenpipe.com).

rag

SID-1 is an agentic search model that is 24x faster than GPT-5.1-high, 374x cheaper than Sonnet 4.5, and achieves 1.9x higher recall than traditional RAG pipelines. Here's how we trained it using large-scale RL on turbopuffer.

↯ Sonnet 4.5gpt-5ragsonnet+1

I've been going down a rabbit hole tinkering about what actually happens after you ship an LLM-powered app, and I'd love to hear how others here handle it… A few things I keep getting stuck on: Continuous optimization. Once your app is in…

rag

rag

​ hi folks, I am new to the community and I have gone through the rules and I hope I am not breaking any of them with this post and will try to maintain 1/10 ratio. For building RAG, there are many tools out there each solving a piece of t…

rag

Hi HN. Strata is a real-time markdown editor built around document portability across agentic workflows.

ragchatgptmcp+1

I tend to use Claude for a lot of research and I also increasingly worry about things like misinformation or things in the model I can't audit. So, I'm building my own all in one RAG with big datasets like all of Wiki, research papers, all…

↯ Qwen 3.6moerag

The same question lands on this sub a few times a week, and the standard answers (RAGAS, DeepEval) are correct but stop one layer short of what you actually need once your app leaves a notebook. Wanted to lay out the full picture for anyon…

ragagentic

I've been pretty unsatisfied with web search options for local LLM/RAG systems. Most setups either rely on paid APIs like Brave, or meta search scrapers like SearXNG.

rag

Every LLM conversation starts from zero. RAG helps, but it can't learn from what's happening right now.

↯ Ollamaollamaragmcp+2

I am doing financial modeling for the startups and feasibility reports for the new companies for more than a decade now, I started playing with Lovable 6 months ago, then somebody introduced me to the VSCode with claude, it’s like a superp…

rag

Lately I’ve been working on a few enterprise AI use cases, and one thing keeps coming up. We spend a lot of time trying to improve retrieval.

rag

Back in September, we at Halcyon shared our plans to build five data subscriptions in the coming months. If you are reading here, you’ve probably been along the journey with us: from gas power plants, to large load tariffs, to utility rate…

rag

Looking for some advice here because I made a hasty purchase. "Cut your losses and move on" is totally a reasonable answer, but I figured I'd look for some additional help. So, I just started working on a local RAG pipeline with about 15,0…

rag

How we taught a small LLM to throw away 68% of our RAG context Pruning agent context down to what the answer actually needs, while keeping 96% of recall by Lars Baltensperger Kapa builds AI assistants that answer complex questions over lar…

rag

I'm the CTO at System1. We own Dogpile.com.

ragmcp

Ask the SpaceX IPO filing like an analyst. Grounded across 84 indexed sources, including prospectus summaries, risk factors, MD&A, launch vehicle pages, Starlink materials, xAI/X references, charts, and image exhibits.

rag

This tool searches real NZ Tenancy Tribunal decisions published by the Ministry of Justice. Decision links point to NZLII.

rag

We Built a Memory Engine. The Brain Told Us How.

rag

https://github.com/user-attachments/assets/e4897391-c5a8-4391-93c3-9f8b76155f11 Setup your local LLM stack effortlessly. Starts fully configured Open WebUI and Ollama harbor up Now, Open WebUI can do Web RAG and TTS/STT harbor up searxng s…

↯ Ollama↯ Vllmvllmollamarag+1

My RAG agent hallucinated. Not because the LLM was bad — because the retrieval was feeding it noise.

rag

Claw-Coder is an AI agent that runs locally on your laptop and has access to powerful tools instead of configuring claude or codex to use a local model just use claw-coder. Why was claw-coder created?

ragcursorcodex

I've kept a personal diary since 2019. Last week I fed 200+ entries to an LLM and asked it how I've changed over 7 years.

↯ Fine Tuningfine-tuningrag

Okay so i've been stalking this sub for some time and i run the occasional small 2-8b model on my laptop (not the best) for fun but say my role at a company is to set up a local LLM since we obviously don't want confidential data going to…

↯ Gemma↯ Qwen 3.6gemmaragqwen

The weekend of last week I built chat.betterdb.com as a RAG over Valkey/Redis/Dragonfly docs. The goal was to eat our own dogfood and test publicly our caching libraries.

ragopenai

A small tool I made for myself (ast-outline), sharing in case it's useful... still experimenting with it.

ragcursorclaude-code

We built a corpus of 500,000 documents simulating a real company, and then let RAG systems compete to find out which one is the best. Introducing EnterpriseRAG-Bench, a benchmark for testing how well RAG systems work on messy, enterprise-s…

rag

Hi all, I'm an AI automation engineer who builds systems that replace manual work, scale outreach, and turn workflows into revenue. I have sent out working systems for managing leads to CRM, finding real estate deals, sorting emails with A…

ragagentic

Hey Everyone, Over the last few months, I noticed a massive gap in how we learn about Agentic AI. There are a million theoretical blog posts and dense whitepapers on RAG, tool calling, and swarms, but almost nowhere to just sit down, run a…

↯ Fine Tuning↯ Function Calling↯ Opus 4.7function-callingfine-tuningrag+4

Suppose that for now you only had access to the frontedn of a RAG system and you don't know how does the backend works, but you need to improve confidence of retrieved results. How do you design this process to be able to improve it?

rag

rag

Hey all, we’re a mid-sized company (~70 people) and currently planning to bring a lot of our workloads on-prem instead of relying on cloud APIs. The goal for the moment is to run small to mid-sized models in the range of 30B like Qwen3.6 o…

↯ Qwen 3.6ragagentic

Hey so I am just going to make a post to see what almighty reddit has to say but I am trying to get claude to connect to an Obsidian vault so it can help me reference lecture notes, textbook theory, past claude convos, and projects and sof…

rag

[]() Documentation · Quickstart · API Reference · Changelog · Discord · Report a Bug Build production LLM apps with 2 dependencies. Async-native RAG, Agents, and Graph workflows — no magic, no SaaS, no bloat.

rag

I’ve been building a small tool called oamc around a workflow I wanted for personal research and long-running project memory. The basic idea is: instead of repeatedly querying raw notes/documents, sources get ingested into a maintained mar…

rag

raggy A lightweight CLI tool for Retrieval-Augmented Generation (RAG) over local documents built with LangChain, Chroma, and Ollama. Hybrid database (vector + BM25 index) and embedding generation run fully locally.

↯ Ollamaollamarag

RAG in context A notebook about retrieval-augmented generation: getting a language model to answer questions from documents it was never trained on. Search does the work: find the few paragraphs most likely to hold the answer, and paste on…

rag

Hi everyone, I am a professor in AI, and I often build AI apps as a hobby and for academic projects. I felt that I was wasting too much time setting up backends, DB, and secrets for each app, and testing the prompts, updating them, and red…

rag

hRAG Hybrid retrieval with receipts. 00 · 01 The platform, answering actual screenshots of the live thing… the 512K-document playground, streamed answers, citations you can open.

rag

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↯ Fine Tuningfine-tuningrag

Production-Grade 10-Node Agentic AI featuring ReAct Web Search fallback, Human-in-the-Loop reasoning, and WhatsApp Meta Cloud API Webhook Integration.

ragmcpagentic

Hi, inspired by Andrej Karpathy's autoresearch, I built autoretrieval, a small framework that lets an AI agent improve a RAG pipeline by running experiments against an evaluation dataset. The agent edits the retrieval pipeline, runs an eva…

rag

Today we released an open, Valkey-native context layer for AI agents as part of our packages at BetterDB (agent memory, semantic + multi-tier caching, typed retrieval) that run on a Valkey instance no matter where it is - no vendor lock-in…

rag

Open to Work I build production AI systems that add intelligence to processes. My work includes Closed-Loop AI-native systems, RAG, AI agents, agentic evaluations, guardrails, and enterprise integrations using Python, TypeScript, React, No…

raggeminiopenai+1

RAGProbe Find the questions your RAG pipeline will fail on — before your users do. RAGProbe analyzes your chunk corpus topology (the graph of how chunks relate to each other in embedding space) and generates adversarial questions targeting…

rag

Vedana Open-source multi-agent RAG over a knowledge graph. Instead of guessing answers from text similarity, Vedana agents navigate a typed graph step by step — issuing Cypher queries, running vector search, verifying sources, and assembli…

rag

Discover newly released YugabyteDB 2026.1 and YugabyteDB AMP (Agentic Multitenant PostgreSQL): a true serverless, scale-to-zero PostgreSQL where every agent gets its own real, isolated database starting at a fraction of the cost of a core.…

ragagentic

Raidho ᚱ A coding agent that plans with one model, executes with another, and remembers what it learns. Most coding agents are one model in a tool loop.

rag

For me and my family, it get really annoying having to reshare information each time you create a new LLM chat. Therefore, I decided to create local-memory-mcp, a local MCP that allows LLMs to read and write to a RAG.

ragmcp

Building a Personal RAG Chatbot in a Few Days: Learning by Engineering How I built a small personal RAG chatbot using FastAPI, PostgreSQL, and Docker as a practical engineering exercise. Building a Personal RAG Chatbot in a Few Days: Learn…

rag

Verbatim-RAG Extractor Chill, I Ground! 🌶️ Model Name: verbatim-rag-modern-bert-v2 Organization: KRLabsOrg Github: https://github.com/KRLabsOrg/verbatim-rag Overview The Verbatim-RAG Extractor is a query-conditioned token classifier that h…

rag

Composition Hallucination in Retrieval-Augmented Generation: A Failure Mode and Benchmark Protocol Description Retrieval-Augmented Generation (RAG) is commonly motivated by the idea that language models answer more faithfully when relevant…

↯ Hallucinationhallucinationrag

We did a deep-dive comparison of the 8 major open-source AI agent frameworks as of mid-2026: 🔹 LangGraph — Best for complex state machines & DAG workflows 🔹 CrewAI — Best for multi-agent role-playing teams 🔹 AutoGen — Now in maintenance mo…

ragopenai

As some other fellow lllmers I've discovered few days ago that the amazing llama.cpp project has just added native tools functionalities into the server. After having enabled the relative options into llama-server and played a bit with the…

↯ Llamallamarag

How Large Language Models Work A primer on LLMs: from tokens, embeddings, and Transformers to training, RAG, tool calling, multimodality, and end-to-end product flows. A large language model is not a human mind simulated in software.

rag

I gave this talk twice in one month: at O’Reilly’s Context Engineering Event and at Abi Aryan’s Maven course on LLM inference at scale. After being blasted with questions, I realized something: GraphRAG isn’t a retrieval algorithm, it’s a…

ragmcpagentic

As AI agents become more autonomous and persist memory across sessions (RAG indexes, conversation history, vector stores), there's a growing attack surface that most people aren't thinking about: memory poisoning.An attacker can plant mali…

↯ Securityprompt-injectionragsecurity+1

https://arxiv.org/abs/2603.23516 https://github.com/EverMind-AI/MSA If verified, rag is no more needed.

rag

Anchor catching the failure mode where RAG retrieves the refutation and still gets it wrong Ask vanilla RAG over Duval, Goeckner, Klivans, and Martin's 2015 paper "A non-partitionable Cohen-Macaulay simplicial complex" this question: What…

rag

moving my current research workflow from a single generalist agent to a multi-agent setup (MAS), and the projected token usage is terrifying. some benchmarks suggest it can be up to 15x more expensive than a standard chat exchange.

rag

I'm kinda tired of kinda doing rocket Science to have a local agent. Trying to Figure out why its out putting garbage , Then Getting it's output to to stream through my UX layer Properly , Getting it to call tools properly.

rag

Hi HN, I'm a big fan of AI's ability to provide personalized tutoring. So, lately, I have been using my Antigravity IDE (you can use any agentic harness) for personal learning.

ragmcpagentic

I've been going down a rabbit hole trying to solve LLM memory. the problem where every session starts blank and your agent has no idea what it learned last week.

ragmcp

Been looking for a self-hostable way to maintain a personal knowledge base from research docs without the complexity of setting up a vector database, writing chunking logic, and babysitting embeddings. Ran into OpenKB this week and it's cl…

vector-databaserag

I’ve been auditing quite a few RAG codebases lately, and it’s surprising how often the hallucinations creep in even when the setup looks decent on paper. A lot of the trouble starts with chunking.

rag

Prototype of an agentic SDLC workflow running inside VS Code + Copilot. Simple loop: task → reasoning → audit → memory → RAG refresh.

↯ Copilotcopilotragagentic

I'm building a small text-based game where the gameplay loop is "talk an NPC into revealing a secret." It's basically a 20+ turn roleplay stress test: the model needs to stay in character, remember what the player said earlier, and refuse…

↯ Tool Use↯ Llama↯ Qwen 3.5tool-usellamarag

been spending ages lately trying to tighten up citation + traceability in RAG-based research workflows, and I’m starting to feel like “retrieval” and “verifiability” are still pretty loosely coupled in most stacks.Typical setup (vector sea…

↯ Tool Usetool-userag

I've been using Claude Max for a few months now, and Projects have been central to my workflow. I use two Markdown files in a long-term project that I update regularly — they're essentially living documents that grow over time as I add not…

rag

Hey everyone, I’m currently working on turning a fairly large and structured financial website into an AI-powered knowledge assistant (RAG-based). The site itself isn’t trivial, it has multiple product categories (cards, loans, accounts),…

rag

rag

I've been presenting at local meetups about Context Engineering, RAG, Skills, etc.. I even have a vbrownbag coming up on LinkedIn about this topic so I figured I would make a basic example that uses bedrock so I can use it in my talks or v…

rag

Israel-Lebanon Ceasefire Agreement DEEP DIVEIn-depth analysis of the 10-day, US-brokered ceasefire agreement established between Israel and Lebanon. A pivotal 10-day ceasefire agreement between Israel and Lebanon officially went into effec…

rag

Processing img 8ofni1q6dpvg1... Hello everyone, I’m building an offline RAG system for my company, we are trying to run an app that retrieves infromation from two manuals in an android tablet with the idea of an AI to provide precise answe…

↯ Gemmagemmarag

I built Zuver, the generic Agentic AI framework for scalable, reliable, even on-edge AI applications and Agents. It's completely written in Go, which lowers the RAM usage to around 6MB, compared to other Agent framework that's usually arou…

ragagentic

Hot takes turn complicated topics into one confident sentence. That makes them great for engagement, but not necessarily for understanding.

ragmcp

So I've been working on a project (Make0 AI) for a long time where I wanted to give common context to both Codex and Claude Code. The problem is simple - while working on Claude and after exhausting the daily/weekly limits I've start again…

ragcodexclaude-code

BindAI Build AI software. BindAI is an open-source, modular Python framework for building AI applications with reusable components for agents, tools, workflows, memory, knowledge and RAG, model providers, integrations, automation, and runt…

rag

post-graph-rag Graph RAG with a memory of time — on the PostgreSQL you already run. post-graph-rag extracts entities and relations with an LLM, stores them as a property graph beside pgvector embeddings, and answers questions by fusing vec…

rag

Hi everyone, I built Nightshift: An agent-agnostic rust-cli tool that orchestrates completion of GitHub Issues. When Openai and Anthropic released `/goal` a couple months ago, I was really excited to try it for long-horizon tasks.

ragopenaianthropic

Hey HN. I'm Christian, one of the founders of Frigade (YC W23).

rag

Strips cookie banners, navigation menus, and ads. Built-in Tier 2 Stealth Headless Browser bypasses Cloudflare Turnstile, JavaScript challenges, and anti-bot walls.

rag

📚 librarian Turn any ebook into clean, token-counted Markdown chapters — let Claude read your bookshelf over MCP and catch it misquoting your books. Deterministic RAG: no embeddings, no network, no API keys.

ragmcp

Discover insights from your knowledge base Upload PDFs, Word docs, or paste URLs. Get a branded AI assistant on your own URL, or embed it on your site — with RAG search, citations, analytics, and a curated questions page.

rag

Docs Reference A library for referencing URL documentation with a local RAG system. Written in Common Lisp.

rag

Nowadays, most people seem to over-engineer their RAG stack. They jump straight to embeddings, vector databases, and reranking pipelines.

rag

Scaling RAG: Building an Efficient Pipeline for 500k Chunks with Gemini 2.5 and Context Caching 1. The Architecture To process a volume of approximately 2GB (roughly 500,000 text chunks), we need a high-performance pipeline: - Extraction:…

raggemini

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rag

RAGless — Deterministic Retrieval-Only Q&A System RAGless is a retrieval-only question-answering system with zero LLM calls at runtime. Source documents are converted into self-contained informational blocks, indexed into a local vector da…

rag

VectorPrism Positional Subspace Multiplexing (PSM) & Intent-Gated 2-Stage Retrieval Engine for High-Scale RAG. One contiguous 1024d tensor.

rag

AI systems engineer who designs and ships production AI end to end - RAG pipelines, agentic assistants, measured retrieval quality, and multi-provider LLM infrastructure, with hands-on QLoRA fine-tuning. Built a private AI platform (Archit…

↯ Fine Tuningfine-tuningragagentic

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rag

rag-staleness-check Read-only staleness / orphan / duplicate / retrievability checks for a single pgvector, Qdrant or Chroma index - the open-source half of the RAGproof "decayed RAG index" teardown (full writeup + multi-engine ledger-veri…

rag

A reproducible teardown of a vector index after 13 months of real churn across pgvector, Qdrant and Chroma — 90% of live chunks failed at least one check.

rag

Case Study Debugging and Detoxing a Long-Term Memory RAG A conversational AI with persistent memory had quietly poisoned its own recall over several months. This is how we found it, measured it, fixed most of it, and what we deliberately l…

rag

About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features NFL Sunday Ticket © 2026 Google LLC

↯ Fine Tuningfine-tuningrag

Warden Permission-aware retrieval and agent-authorization gateway for AI systems. Warden enforces relationship-based, deny-aware, cross-tenant document permissions inside the retrieval path of agentic RAG systems — behind a fail-closed sec…

ragagentic

BLACKBOOK Home Portfolio About Services Experience Contact CV EN DA Journal | Privacy Policy | Copyright © 1996-2026 BLACKBOOK BLACKBOOK Home Portfolio About Services Experience Contact CV EN DA Journal | Privacy Policy | Copyright © 1996-…

rag

Mnemo AI A local agentic AI assistant with MCP (Model Context Protocol) integration, RAG capabilities, and intelligent conversation management. Built on LangGraph with LangChain for multi-provider LLM support (Ollama, Amazon Bedrock, OpenA…

↯ Ollama↯ Model Context Protocolmodel-context-protocolollamarag+2

rag-redteam Red-team your RAG pipeline for prompt injection and source-document leakage, right in CI. RAG systems have an attack surface that general LLM scanners miss: the retrieved documents themselves.

↯ Securityprompt-injectionragsecurity

Standard RAG retrieves documents and lets the LLM generate field names freely — which means it will invent fields your schema never had. AK-RAG (Attribute Knowledge RAG) indexes your governed attribute catalog instead, and the LLM can only…

rag

graph-guard A knowledge graph that makes RAG answer the hard questions — the ones where the answer is spread across several documents — plus a measured, honest account of when the heavyweight semantic stack is worth its cost and when it is…

rag

Specialising in LLMs, MLOps, and production ML systems.

rag

ragit Local RAG CLI to chat with any folder of documents using Ollama. Install cd ~/ragit python3 -m pip install -e .

↯ Ollamaollamarag

How this works is that I extracted knowledge from PGs publicly available essays. Instead of using RAG I used a state map approach, if you are keen about the extraction method I can share more in the comments.

rag

In 2022 - I thought that we got closer to AGI with LLMs, but it turned out LLMs were more like the engine of intelligence than intelligence itself. In 2023 - I started to understand how important memory is to the system, and the intelligen…

rag

FAME Knowledge Agent Gateway 中文优先。English follows. 中文简介 FAME Knowledge Agent Gateway 是一个面向 Agent 的万用外部插件：它把图谱知识网、项目工程记忆、工具调用治理、FAME 路由反馈和可视化工作台放在同一套外部运行时里。 它不是普通 RAG，也不是普通聊天记忆。它的核心目标是让 Agent 在真实工程中先对齐目标、再按知识路线思考、再受控调用工具、最后把成功、失败、教训和证据写回外部记…

rag

Ask Channel AI (http://askchannel.ai) allows you to quickly find relevant videos, quotes, and timestamps from any YouTube channel. Channels that are not yet indexed or imported can be done so by signing up, and imports are typically pretty…

rag

Tilores is cloud-based entity resolution software used for fraud-detection, AML, RAG - but being cloud based makes it difficult for people to test as they are reluctant to upload sensitive data. To fix that we built a downloadable version…

rag

Production RAG without the 9-month build SOTA on public retrieval and OCR benchmarks. Three endpoints, one API key.

rag

Correctly conceptualizing and handling vectorization in knowledge graphs January 26th, 2026 Embeddings are now a default building block in modern data services, powering semantic search, retrieval-augmented generative AI (RAG), clustering,…

rag

Install neo-mcp, register NEO with Claude Code, and delegate RAG audits, fine-tunes, evals, and pipeline debugging without leaving the terminal.

ragmcpclaude-code

CEM888.AI Enterprise-grade localized AI infrastructure and sovereign computing environments. CEM888.AI is advancing the future of private, high-performance artificial intelligence systems.

rag

Phlox A feature-rich, ChatGPT-style, self-hostable AI assistant. Phlox is a self-hostable chat application with an agentic harness, document RAG, code execution, and MCP integration — running over any model provider: AWS Bedrock or any Ope…

ragchatgptmcp+1

The use of retrieval-augmented generation (RAG) to retrieve relevant information from an external knowledge source enables large language models (LLMs) to answer questions over private and/or previously unseen document collections. However…

rag

I'll keep this post short and sweet, we have seen several steps in the evolution of LLM (large language model) usage. 1.

ragmcpagentic

How to Build an Agentic RAG with RubyLLM and Rails I run a RAG application for Italian pension and tax consultants. Users ask questions about INPS, professional pension funds, laws and regulations, and the app answers using a knowledge bas…

ragagentic

How we used StarRocks, Gemini, and tool-based retrieval to power grounded Q&A in a developer community Slack. 9 min read 7 hours ago Author:Billy Chang, Software Engineer at Phoenix AI Press enter or click to view image in full size StarRo…

vector-databaseraggemini

AI-powered knowledge base for your Terraform modules. Index, search, compose, and audit - all from one place.

ragmcp

Your AI agent reads untrusted content. Every web result, document, and API response your agent retrieves goes straight into the model's context window - unscreened.

rag

Overview Store, search, and chat with web page content locally. AI chat (BYOK), full-text search, markdown export, and optional RAG endpoint.

↯ Ollamaollamarag

OWASP Agent Memory Guard: Stop AI agents from being weaponized through their own memory AI agents keep memory across sessions. Conversation history, vector stores, scratchpads, and RAG indexes persist between runs, and anything written int…

rag

I know this sub is focused on local models but the architecture behind this applies to any LLM-powered coding agent, not just Claude Code. The problem: when you give a coding agent a large set of rules and standards, two things break.

ragclaude-code

A pattern I keep seeing in agentic RAG systems: The agent is smarter than the retrieval layer. It can notice that context is stale.

vector-databaseragagentic

Search Router is a web search API built for AI agents and RAG systems. We built it internally at first, when working on AI tools.

rag

The whole point of AI Agents is that they can *do* things. For this, they use API keys, GitHub tokens, database passwords, OAuth tokens, etc.

↯ Securityprompt-injectionragsecurity

Agentic RAG systems that equip language models with dozens to hundreds of tool definitions face a critical resource conflict: tool schemas consume the same context window needed for retrieval-augmented generation. We present the first syst…

ragagentic

I wanna know how people here are handling security once local models move beyond chat.....Running a model locally feels safer because the data does not leave your machine or your infra. That is a real advantage.....But once the local model…

↯ Securityprompt-injectionragsecurity

I’ve been trying to understand why coding agents seem to work better than most non-coding agents. Maybe the thing coding agents have that most other agents don’t is the repo itself.

rag

NuExtract3 is a unified 4B vision-language reasoning model for document understanding. It combines strong structured information extraction with high-quality image-to-Markdown conversion, making it suitable for extraction pipelines, OCR, a…

↯ Vllmvllmrag

My rag I've been building is much in response to having a LLM that I feel more confident in knowing where the knowledge base is coming from especially after the Open AI deal with the Pentagon. So, when I saw "uncensored" heretic models, I…

↯ Qwen 3.6rag

Hey everyone, For people building agents with LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, Claude MCP/SDK, Google ADK, or LlamaIndex — how are you managing LLM API costs? Agent workflows can get expensive fast because of: tool calls retr…

↯ Llamadeepseekllamarag+5

So far, AnythingLLM works well for me when i copy files over to docker folder (so originals can't be erased/modified), and i have LLM do a text search. RAG I tested but with number of files and specificity, just searching for file names an…

rag

Your embedding model doesn’t understand your data INTERNALS.md #3 · It never did. Here’s what it actually does, and why that matters for every RAG system you’ll ever build.

rag

Everyone is talking about context management. RAG pipelines, memory systems, knowledge graphs, long-context windows.

rag

https://nanorag.nb1t.sh/ Important: Please choose correct namespace from top-right dropdown. Available docs/namespaces: Cloudflare, Nextjs, and Dodo-payments (default).

ragagentic

RAG vs. Fine-Tuning — The Question Every AI Builder Gets Wrong AI models don't know your private data.

↯ Fine Tuningfine-tuningrag

I’ve been thinking about AI agent security less as a prompt-injection-only problem and more as a runtime supply-chain problem. In many deployed agents, the model is no longer just generating text.

ragmcp

I keep seeing agent memory implemented as: Extract facts/preferences from conversation Store them Retrieve top-k before each response Inject them into the prompt This works for demos, but it breaks in production because memory becomes poli…

↯ Securityprompt-injectionragsecurity

I am currently looking to get into automation for German Mittelstand and I am now talking to an SME, which got an offer from a consulting firm for document processing automations and trying to figure out if the pricing is normal or inflate…

rag

RetrievalCI Stage: bench-v0 early preview. The methodology, scorecard format, and 9 system adapters are stable.

ragopenai

I want to know that learning these fundamentals is enough to land job or is there something else that i have to learn along with these? Right now i am learning about genAI through campusX and making rag projects.

rag

11 min read Apr 23, 2026 You know these terms alone. Together?

openclawragmcp

argus A RAG-based (Retrieval-Augmented Generation) vulnerability scanner for Go, Python, Rust, npm/Node.js, Maven/Java, NuGet/.NET, and Ruby projects — powered by local Ollama models or any OpenAI-compatible API. No cloud lock-in.

↯ Security↯ Ollamaollamaragsecurity+1

I put together some notes on building agents. I have built agents at scale for a while now and for a few clients, so I thought i would start putting all the knowledge into lessons that might help other people as well.

rag

I see a lot of posts and videos talking about harness engineering, or it could be context engineering, RAG, etc. The thing is, most of them talk about the concepts.

ragcodexopenai+1

This is yet another agent harness and UI and I hope you will have a look and consider contributing. Elumenotion/GuideAnts: GuideAnts Notebooks.

ragagentic

I am trying to understand how the market around RAG is currently, what are it's usecases, how do enterprise companies approach this. Do they just have company related documents which is uploaded to these RAG systems and use it to query the…

ragagentic

Setup: mid-size SaaS, ~3,000 tickets/month, 6 agents drowning. 70% of volume was tier-1 (passwords, billing, where's-my-feature).

ragsonnetagentic

Built a new update for Claude Playground 🚀 Added Mock Tests for learners preparing for the Claude Architecture exam — users can now validate their understanding and test their learning directly on the platform. The goal of Claude Playgroun…

raganthropic

I haven't used Cowork yet - Would it solve my troubles with Project Knowledge files not indexing consistently? I see Projects can now be imported to Cowork, then I'd have my knowledge files hosted on my hard drive?

↯ Coworkcoworkrag

With the rise of SearchGPT, Perplexity, and Gemini, the goal of content is shifting from "ranking on page 1" to "being cited in the answer block." I’ve been working on a tool (https://aibg-intelliagent.com/) that uses a private RAG (Retrie…

raggemini

Two weeks before a Fortune 500 product launch, we told a client to scrap their fine-tuned model and rebuild with RAG instead. They lost eight weeks and $180K.

↯ Fine Tuningfine-tuningrag

https://preview.redd.it/drtw1mjwf7zg1.png?width=997&format=png&auto=webp&s=90b45173c1caba12a10bd4ff4a0a717563be9512 https://preview.redd.it/kk1ayljwf7zg1.png?width=997&format=png&auto=webp&s=f0b210cef867d817891635138f9a531b7e2e2fcc https:/…

rag

NodeMind — Binary Document Intelligence 48× smaller online · 32× smaller offline · up to 100× on images. 75× faster search.

rag

I’m testing a workflow where docs sites get converted into: concise llms.txt index full Markdown bundle cleaned page chunks manifest JSON For people building agents or local RAG systems: do you prefer one giant Markdown file, per-page Mark…

rag

Been using Claude for a few months and hit the same wall everyone hits: years of context stuck in ChatGPT with no real path to bring it over. Claude's built-in memory import is surface-level — name, preferences, tone.

ragchatgpt

I built an AI that tries to answer life’s hardest questions using the Bhagavad Gita. Over the last few weeks, I’ve been building GitaGPT Mentor It’s not just another chatbot.

rag

Sharing a project that just went public: World2Agent (W2A) — an open protocol for the perception side of the loop. Entirely self-hostable, no SaaS, no telemetry, TS SDK, Apache 2.0.

ragmcp

Hi everyone, I’ve been working on a continuity layer for OpenClaw agents, and I’d like to get feedback from people building or running AI agents. The problem I’m trying to solve is that many agents can respond well within a single turn, bu…

openclawrag

RAG Poisoning: When Your “Safe” AI Eats Bad Documents So you built a RAG pipeline. Congrats.

rag

FerresDB Core High-performance vector search engine written in Rust, designed for semantic search, RAG (Retrieval-Augmented Generation) and recommendation systems. Overview FerresDB Core is a Rust vector search engine for semantic search,…

vector-databaserag

I built a browser-only studio for designing and orchestrating MCP agent systems for development and experimental purposes. The whole stack — tool authoring, multi-agent orchestration, RAG, code execution — runs from a single static HTML fi…

ragmcp

Documents are authored, bounded, and self-contained. They carry their own semantic links and can be represented as a wiki or cleanly split into overlapping chunks.

vector-databaserag

I've built a fairly involved system inside a Claude Project: project instructions plus 10 project files that function as a routing system. Trigger words in the instructions point Claude to specific files (instructions, templates, reference…

raganthropic

From an year working in space of developing based pipeline and applications. Have worked enough building data on vector db + chunking + embedding etc., now there is an new trend of using vectorless RAG.

rag

Hey, I'm building an Agentic RAG pipeline and struggling with two decisions: Chunking strategy — fixed-size, semantic, or hierarchical? In an agentic setting where the agent can re-query iteratively, does it make more sense to use smaller…

ragagentic

↯ Model Context Protocolmodel-context-protocolragmcp

rag

vector-databaseragcursor

↯ Opus 4.7vector-databaseraggemini+2

I’ve been working a lot with RAG systems recently, and kept running into the same issue: they retrieve relevant chunks, but lose the relationships between them. This becomes a problem pretty quickly when dealing with real systems (docs, AP…

ragcursormcp+1

We compared four architectures for putting AI agents on websites — RAG bots, API-tool agents(WebMCP), code-writing sandboxes (Cloudflare Agent Lee), and DOM-native execution. Three of them force you to maintain a parallel engineering surfa…

rag

Been experimenting with context compression for local models. Wanted to test how far pure heuristic retrieval can go before you actually need vectors.

rag

If you work in healthcare or just want to see how Claude handles high-stakes clinical reasoning — I built an MCP server for this and wanted to share what made it harder than a typical AI project. EMSy is built on top of Claude and connects…

ragmcp

Built two open source agents: 1. Research agent - searches the web, streams answers with sources (like Perplexity) 2.

rag

It's tax time, so I whipped up a tax doc assistant with our new Ragie skill. Concrete example of agent-assisted development that goes further than toy demos.

ragclaude-code

Hi everyone, I work for a German agency building a RAG chatbot for a law firm. I use Opus 4.6 but it eats up tokens.

↯ Claude 4.6ragsonnetcursor+1

I've been building an AI system to automate parts of the NRC Combined Operational License process: gap analysis against the Standard Review Plan, FSAR strength scoring, and RAI prediction using vector similarity to historical NRC requests.…

ragopenai

Memelang is an AI-optimized query language that significantly reduces token count and model size for LLM RAG. The code below is designed to be copy-and-pasted into your LLM.

rag

RAG fixes what the model reads before it sees the question. Jev fixes what the model can answer before it sees the state.

↯ Tool Usetool-userag

I have been working for a few years in Computer Vision for manufacturing, so I have kinda been in a bubble as far as production goes. I am thinking of diversifying a bit and get to work on the trendy stuff: agents.

ragagentic

SHIFTing Languages in Multilingual RAG Evgeniya Sukhodolskaya ·September 16, 2026 On this page: If you speak more than one language, you know the feeling when the mental switch in your head starts up with the rattling sound of a struggling…

rag

JetBrains AI Supercharge your tools with AI-powered features inside many JetBrains products Building a RAG Pipeline for Semantic Code Search: A Developer Diary and Field Notes Part 1: Parsing, chunking, and vectorization Some time ago, we…

rag

could not extract summary

rag

rag-debugger Intercept, inspect, and fix your RAG retrieval pipeline. Most RAG bugs aren't in your code — they're in your retrieval.

rag

Vector embeddings natively are capable of much more than just retrieval. Even when OpenAI released their initial embedding models, it mentioned that their embeddings are natively capable of Search, Clustering, Recommendations, Anomaly Dete…

ragopenai

julianflux-research Electrodynamic field retrieval and causal routing for AI agents. JulianFlux 🧲: Electrodynamic Reasoning Infrastructure for AI Agents Autonomous AI Agents are failing in enterprise environments because standard Vector Da…

rag

BasinRAG 🌐 English | Português High-Performance Topological Document Retrieval-Augmented Generation (RAG), using dynamical basins of attraction as an index partitioning manifold. BasinRAG indexes and retrieves document passages (PDF, Markd…

rag

Free eBook Essential GraphRAG: Build a hybrid RAG system for every question type By Tomaž Bratanič and Oskar Hane Publisher: Manning Production-ready RAG systems combine structured and unstructured data while connecting entities across dat…

rag

Agentic RAG — Four Working Patterns with LangGraph Four self-contained Jupyter notebooks, each implementing a different way of making a Retrieval-Augmented Generation (RAG) pipeline "agentic" — able to decide, check itself, correct its own…

ragagentic

People use embedding models all the time for rag/semantic retrieval. However, when a newer, more desireable model comes out, there is an expensive (both in time and computational) cost of re-embedding every document in the database.

↯ Qwen 3rag

Hey guys! So we initially built this for our mobile game dev studio to automate handling of support tickets.

rag

Are there any quality system design resources out there like https://github.com/donnemartin/system-design-primer that cover AI Architectures instead - RAG, intelligent document processing, agent pattern etc.

rag

I want to know if it is helpful in coding agents that use RAG and how to use it

rag

Orbit Source code as agent knowledge — multi-repo Git workspaces where agents read, code, and ship → No RAG. No index — real source, not retrieval.

rag

I'm the developer behind CartoType. I’ve been working on bridging local language models with offline mapping, and I have just put together a demo of a completely offline Spatial RAG pipeline running natively on an iPhone.

rag

Real-time, provenance-invalidated context for AI agents & RAG. Build understanding once.

rag

Cernodata Cernodata is an open-source ETL framework for layout-aware PDF extraction, automated quality iteration, structural layout debugging, and dataset generation for RAG and LLM fine-tuning. Parsing PDFs for RAG is a guessing game You…

↯ Fine Tuningfine-tuningrag

Ship RAG to production and watch it fail. Here's what works: semantic chunking, hybrid retrieval, reranking, and how to cut costs 5x.

rag

Hi everyone! I am officially selling full ownership rights (complete Intellectual Property transfer) to my comprehensive 4-in-1 FastAPI backend bundle designed for B2B SaaS and marketing automation agencies.

rag

piFlow 全本地 RAG 桌面应用 · 答案可追溯 · 隐私优先 · 轻量 English | 简体中文 piFlow 是一款检索增强生成（RAG）桌面应用：导入本地文档构建知识库，然后用自然语言提问。解析、嵌入、检索、推理全部在你的机器上（或你指定的服务器上）运行——文档与对话始终不离开你的掌控。生成的答案附带来源引用，可追溯到具体的文档与段落。 主对话由 piFlow Agent（Pi harness）驱动：知识库以 Skill / Tools（kb_*）挂载，并可…

rag

Hello, While experimenting with personal, local RAG app setups, I kept having to (re)generate embeddings and really wanted precomputed embedding datasets that I could quickly pull and use in various environments. I built a library to test…

rag

hubmesh Centrality-aware GraphRAG retrieval planner. Drop-in layer over any vector DB.

rag

I built a personal LLM assistant on Cloudflare Workers + Durable Objects. You specify a category and topic when starting a new conversation, so the backend maintains a summary for each category/topic - building up as more conversations hap…

ragagentic

RagLeap Core The open-source AI business manager. One AI, five channels, one memory.

rag

Benzi is a compiler + runtime tracer + harness and AI agent built to understand code from ground up. Challenging traditional RAG and embedding space approaches, Benzi aims to write code as cleanly as it understands it.

rag

What Actually Happens Inside a RAG Pipeline (And Why “Banking” Is the Perfect Example) A plain-English walkthrough of embeddings, vector stores, HNSW, and why Elasticsearch and vector databases are secretly cousins - even if a few degrees…

rag

AI Evals Aren't Just for Big Tech How a small bootstrapped team measures its AI the way frontier labs do: a practical approach to evals that you can copy. 👋 Hey, Jai here!

rag

I just built this as a Personal/Portfolio project. I genuinely needed it, so I built it and I regularly use it.

↯ Ollamaollamarag

FREE AI SECURITY TRAINING Learn prompt injection through hands-on labs. Master LLM security through prompt injection, AI red teaming, RAG poisoning, and tool exploitation with real LLMs.

↯ Securityprompt-injectionragsecurity

I'm building a SaaS platform in Sri Lanka that handles documents and other sensitive data. Each user can upload their own documents and information, and the platform uses RAG to answer questions based on that user's data.

ragopenaianthropic

Hi everyone, I've been working on a knowledge retrieval system that can create distributable applications for several months and I'd love to share it with you! The issue I've found with asking LLMs for advice is that they are trained on a…

rag

MindBase An AI research assistant that builds and maintains a wiki from your sources. Not RAG-in-a-vector-DB.

rag

After trying out various study tools, I decided to create my own. I wanted a quick and simple way to upload any study material, have the app generate questions, and then set up a daily review schedule.

rag

From keyword to semantics: how search is changing and what it means for site builders Three search models coexist on the web today. They are not evolutionary — the second has not replaced the first, the third has not replaced the second.

rag

So the idea is really simple. If CC need to change a file and run some test, CC needs to: # Turn 1 — apply patch (change package file) # Turn 2 — apply patch (fix the bug) # Turn 3 — apply patch (edit the testing script) # Turn 4 — build #…

ragclaude-code

When a RAG system gives a bad answer, almost everyone blames the model. Usually the right passage was never retrieved — and that changes everything about…

rag

Local Search Agent Give your AI agent a search engine for your local files. What is this?

rag

Vasari Vasari is a local, open source tool for evaluating AI features. Load the traces from any AI agent, chatbot, RAG assistant, or LLM feature.

rag

I developed this tool and tried to keep it minimalist, lightweight, portable and easy to use. Hoping some people will find it useful.

rag

# ORBIT ### Open Retrieval-Based Inference Toolkit A self-hosted, OpenAI-compatible AI gateway for private RAG, natural-language data access, and tool-calling agents — run it in your own environment across 37+ model providers. Quick Start…

tool-callingragopenai

Your legal-tech assistant retrieves a contract and summarises it. The contract contains one sentence you didn

↯ Securityprompt-injectionragsecurity

Hi HN, I am Erik Bethke, game dev that has been making online games for 30+ years (Starfleet Command, GoPets, FarmVille, Mafia Wars). For the last 3 years I have been building Bike4Mind and now I have to put my big boy pants on and 'go to…

rag

Not only are foundation models stuck in the past, but they intentionally produce natural-sounding and varied responses. Both of these can lead to confidently inaccurate and irrelevant output.

rag

Open-source, self-hosted RAG infrastructure built for early-stage startups. High performance, low cost, and simple to run.

rag

MothRag Deterministic, agentic-style multi-hop — research-SOTA parity without the graph you'd rebuild every day. On commodity LLM APIs alone.

ragagentic

About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features NFL Sunday Ticket © 2026 Google LLC

rag

Retrieval is critical in AI agents. To do any task correctly, the agent needs to be able to retrieve all the information that is relevant to the task from its memory.

rag

pdf-struct-chunker Split PDFs into semantically meaningful chunks — without LLMs, without APIs, without cloud dependencies. 🌐 Author: Matthias Nordwig · programmiere.de The Problem Most RAG chunkers blindly split documents by token count o…

rag

This article proposes a reliability framework based on three complementary dimensions: - Failure Dynamics — how reliability changes over time - Reliability Control Surface — where engineers can observe and intervene - Detectability — how e…

rag

Estimate chunk count, embedding storage, vector index size, and monthly database cost for your RAG knowledge base.

rag

synapcores-llamaindex Official LlamaIndex integration packages for SynapCores — the AI-native SQL engine. Replace Postgres + pgvector + Neo4j + Redis with one self-hosted binary.

rag

AI Agent Frameworks, Compared Side-by-side comparison of 15 AI agent frameworks. Filter by use case (RAG, multi-agent, structured output) and language (Python, TypeScript, .NET).

rag

Most RAG pipelines ship raw, sensitive documents over the wire to cloud services just to get them parsed, scrubbed of PII, chunked, and vectorized. BitVanes is a zero-trust, local-first ETL engine designed to solve this.

rag

Vercel Security Checkpoint | sfo1::1781830903-OPQW0grqUdIgsx9p96Wslv3l1RK4f3l0

rag

UCP — Universal Context Pipeline A local-first MCP server that grounds LLMs in your own files. UCP indexes folders on your machine — notes, code, conversation exports — and exposes them to any MCP-compatible client (Claude Desktop, Cursor,…

ragcursormcp

Building Reliable Agentic AI Systems A Case Study in building production-ready agentic AI systems This paper presents the Preclinical Information Center (PRINCE), a cloud-hosted platform developed by Bayer AG with Thoughtworks to address p…

ragagentic

Try Search: https://ethereal-agents.space/search.html Technical Blog: https://ethereal-agents.space/blog/launching-arxiv-scholar.h... We'd love feedback on the retrieval quality, user experience, and overall approach.

rag

🎙️ OmniPod Chat with 936 podcast episodes. Every answer cites its source.

rag

We have all seen RAG and Graph Knowledge, but in your opinion or if you know of some cool project, what’s the next innovation that could a hierve true perpetual memory and true personalization???

rag

Free soccer RAG MCP Server. Connect it with Claude or your favorite agent and kickstart your soccer research.

ragmcp

Path traversal jailing, SSRF blocking, VBA macro stripping, zip-bomb detection, multi-column PDF, and PII redaction. Everything MarkItDown skips.

rag

Turbo Rag Turbo-fast RAG for Frappe (v14) using TurboVec License MIT

rag

Skynet: Towards Synthetic Neurobiology The original idea was a joke. I was looking at LLM loops and thinking about how they map onto Elixir’s actor model — GenServers that receive messages, process them, maybe spawn new processes.

rag

Prompt Injection in RAG Agentic Systems Real risks and production mitigations Imagine you built an AI assistant for your team. It answers questions using internal documentation: Jira tickets, Confluence pages, HR docs.

↯ Securityprompt-injectionragsecurity+1

chunks-sync Incremental synchronization for RAG pipelines. Most RAG ingestion pipelines re-embed every document whenever a file changes, even if only one paragraph was edited.

rag

Retrieval-Augmented Generation (RAG) has become an essential method for mitigating hallucinations in Large Language Models (LLMs) by leveraging external knowledge. Although effective for simple queries, traditional RAG struggles with large…

rag

Introduction ext-infer is a PHP 8.3+ extension that loads a GGUF model and runs LLM inference inside the PHP process via llama.cpp. PHP-native semantic search, RAG pipelines, and CLI / worker inference run without shelling out to Python or…

↯ Llamallamarag

Sign In / Create Account Enter your API key to sign in New user? Create a free account with 5 extractions Account created!

rag

HPPIE fuses persona modeling into the RAG pipeline to deliver patient-specific health content. 2nd of 300+ at a Global AI Hackathon.

rag

After a lot of work I present Digger Solo 0.5.0 - the AI file explorer that respects your privacy (everything runs locally). Demo video: https://vimeo.com/1198414414 New features: - LLM Chat with RAG (bring your own OpenAI compatible API k…

ragopenai

In this article, author Aaditya Chauhan discusses the limitations of RAG pipelines based purely on vector search and how an internal omni-search application using Reciprocal Rank Fusion (RRF) that combines BM25 and vector results, can enha…

rag

Hey HN, we’re Soami, David, and Achyut, co-founders of Extract. Extract parses documents into structured data (text, tables, and figures).

rag

How we index images for RAG Reading the screenshots, diagrams and tables in technical documentation for LLMs by Matteo Bortoletto Kapa builds AI assistants that answer questions from technical documentation. The knowledge bases we process…

rag

verifiable-rag Document-grounded Q&A with sentence-level citations, NLI verification, and calibrated refusal. Status: pre-alpha · v0.5 launch sprint · interfaces are still subject to change 📚 Full documentation at firish.github.io/rag-rack…

ragsonnet

AI-powered document analysis that runs 100% locally on your Mac. Analyse contracts, engineering specs, and sensitive data without the cloud.

rag

Retrieval-augmented generation makes language models far more useful by grounding them in real data, But it also raises a hard question: who is allowed to see what? This post shows how authorization can be enforced before retrieval, ensuri…

rag

VDF.AI is the on-premise AI agent platform for enterprises that need governed multi-agent orchestration, private RAG, LLM routing, and full data sovereignty — without the cost or lock-in of cloud AI.

rag

We're hiring Paper Blog Audio 4 / - Hide Tools Ctrl + / Open Tools A Fixed-Budget, Cluster-Aware Standard for LLM-as-a-Judge Evaluation: A Multi-Hop RAG Stress Test Assistant My Notes Comments Similar

rag

sometimes when i scroll social media i see stuff about knowledge graphs. it crossed my mind that I do something similar.

rag

Change https://github.com/owner/repo → https://cgc.codes/owner/repo A standard GitHub URL can be instantly transformed into a CodeGraphContext (CGC) graph URL, unlocking architecture visualization, code navigation, dependency exploration,…

↯ Copilotcopilotraggemini+2

Anyone here working at Gnani AI or knows someone there? I got an offer for the AI Prompt Engineer role and wanted to know how the work culture is.

rag

People are warning me about the prompt-processing speed of a MacBook Pro M5 Max with 128 GB RAM. My main concern is prompt ingestion / prefill latency and large-context handling — not raw token generation speed (which I think is OK).

↯ Qwen 3.5↯ Qwen 3.5moeragqwen+2

Tlamatini A local-first AI developer assistant that goes beyond chat. Run it on your machine with Ollama.

↯ Ollamaollamarag

Lately it feels like every problem gets the same answer: “Just build an AI agent.” I had lunch recently with people outside tech, and someone mentioned spending hours replying to customer chats at work. Immediately another person said: “Wh…

rag

Lexical search with grep is fast and precise, but it breaks down at enterprise scale. Learn when to use grep, semantic search, or a hybrid RAG approach to build AI agents that can search any corpus, in any format, at any size.

rag

Anyone here from a mid-size or enterprise company using AI for internal IT support workflows like password resets, account unlocks, MFA resets, software access requests, etc.? We’re exploring AI-driven employee support internally and I’m c…

↯ Copilotcopilotrag

19+ tools — no cloud, no API key, no subscription. All in one AppImage / Installer.

rag

About me: I am an AI Product Engineer specializing in building autonomous agentic workflows. Recently, I built 'Jarvis', a multimodal autonomous agent featuring near-zero latency inference using Groq SDK and complex RAG pipelines.

ragagentic

Hey, I'm a data engineer looking to transition into AI engineering. I'm looking to learn and build a resume with some projects.

rag

Hi HN, When Openai released `/goal` earlier this month, I was really excited to try it for long-horizon tasks. But after using it, it didn't blow me away and i did some digging and found a major architectural flaw when using it for complex…

ragcodexopenai

Astrum Verum Composition-episodic cognitive memory for AI agents — and an honest record of how it got here. Astrum Verum is a research project containing two distinct phases of memory architecture development.

rag

If a localization pipeline uses retrieval augmented generation to inject glossary terms into the model's context window, it has a retrieval recall problem that has never been measured. The pattern is universal: embed the input text, cosine…

retrieval-augmentedrag

I was wondering if it would make local llm better at coding if it has access to the latest documentation available through a RAG. I'm specifically interested in python.

rag

I would rate my AI skills between beginner and intermediate. I know how to use tools like ChatGPT and GitHub Copilot to build a chatbot with a system prompt.

↯ Copilotcopilotraggemini+1

I've been building agentic tooling at work and wanted to share one pattern that worked. Instead of a chatbot that only retrieves and answers, I wired custom MCP servers in as the action layer, so staff trigger live workflows (create record…

ragmcpagentic

I leave this field in 2023 return back in 2026 and I see that only progressive development in coding agents, but some production solutions it’s just tools rag and maybe mcp that in general the same as tool. I thought it will be super leap…

ragmcpagentic

medium.com Performing security verification This website uses a security service to protect against malicious bots. This page is displayed while the website verifies you are not a bot.

rag

Just started in a new bigger company. Suppose to accelerate the adoption of AI.

rag

Tiered + page-role-aware RAG retrieval results across 3 corpora with very different content density: Workspace Sources Chunks HIGH MEDIUM LOW REJECTED Intercom 188 941 96 200 541 104 HubSpot 251 1705 40 508 1153 4 KPMG 53 209 3 14 127 65 (…

rag

I built ztok, a tokenizer library focused on being fast and format-agnostic for local pipelines. - Loads what you already have — .tiktoken, HF tokenizer.json, SentencePiece .model, TokenMonster, Mistral Tekken.

↯ Mistralmistralrag

I'm about to introduce gemini filesearch to my company to handle all the RAG related operations but not just internally, I'm fixing the projects VS stores logic to be able to scale this up to thousands of small clients. Has anyone used gem…

raggemini

I've spent the past year building a site-search product and watched maybe 50 teams plug their docs into a vector DB, expect magic, and end up debugging why the LLM is lying. Its almost never the LLM.

rag

I have been building an open catalog of agent patterns and the frameworks that implement them. It is a pattern language in the Christopher Alexander sense, mapped onto the current agent landscape.

ragqwenopenai

I run a second brain on Obsidian, Readwise, NotebookLM, and Claude Code. For each topic, I build a scoped wiki structured as the LLM Knowledge Base Andrej Karpathy proposed.

raggeminicodex+3

Been working on AI-assisted workflows for tokenization platforms recently, and I’m honestly surprised by how useful agents are becoming in complex financial processes. Some areas where they’ve helped a lot: onboarding automation document u…

rag

Hey everyone, I wanted to share a project I've been working on called Glia. It is a 100% offline, local-first RAG and memory layer designed to connect your AI web chats (Claude, ChatGPT, DeepSeek) with your local developer tools (Claude Co…

↯ Ollama↯ Windsurfwindsurfollamadeepseek+4

I'm working on a Fetch API for RAG, agents, and web ingestion workflows. Think Firecrawl/Jina Reader-style URL-to-markdown or clean-text API, but with one extra signal layer: page labels for content category and page structure.

rag

How we engineered RAG to be 50% faster - Written by - Michal Korbela - Published - Last updated ListenListen to this article RAG improves accuracy for AI agents by grounding LLM responses in large knowledge bases. Rather than sending the e…

rag

Vector search looks easy, until you hit production scale. I'm super excited to share a new episode of the Weaviate Podcast with Başak from @bookingcom on production-scale vector search, RAG, and agentic AI with @weaviate_io!

ragagentic

Most of us build RAG the same way: follow a tutorial that embeds a handful of PDFs, stores the vectors in a local Chroma instance, and chains everything together with LangChain (if that’s still a thing). The demo works.

rag

Presight.ai is a publicly listed company with various projects in the field of big data analysis and ML models application. Our solutions work domestically and internationally.

ragagentic

For context, I posted on Medium a while back about burning through Claude Code's weekly limit in 3 days. The token bleed problem from that post is what kicked off this project.

ragagenticclaude-code

Hey Everyone, The AI engineering job market has shifted massively in the last 6 months. Interviewers are no longer just asking "how does a transformer work?" or "how do you write a good prompt?" They want to know if you can architect produ…

ragagentic

The more I work with long-running agents, the more it feels like most “AI memory” today is just retrieval with nicer branding. Everything works in demos: vector DBs RAG summaries context packing knowledge graphs But after enough real usage…

rag

🧪 RAG‑LCC — Experimental RAG Under Constraints RAG‑LCC is an experimental Retrieval‑Augmented Generation (RAG) lab focused on understanding and controlling retrieval and context assembly under real‑world constraints: limited context window…

rag

Most local LLM use cases I see are chat, coding, and RAG. But with vision models getting better and faster on consumer hardware, I feel like there is a lot of untapped territory.

rag

Agents have a search problem across the whole stack: web search, RAG, tool discovery, skills/workflow loading, and even context compaction.

rag

Most “AI agent” tools in 2026 still feel like glorified chatbot wrappers. I spent the last few months testing different agentic AI workflow builders for real-world automation use cases (multi-agent workflows, approvals, integrations, long-…

↯ Copilotcopilotragagentic

Been working with a few teams integrating RAG into internal tools, support bots, document Q&A, contract search, and I keep running into the same thing nobody warns you about when you're following tutorials. The basic retrieve-then-generate…

rag

RAG gives you search over a corpus. Useful.

rag

I'm the author of this thing, disclosure up front. Been hanging around this sub lately on cache invalidation, MoE memory tradeoffs, long-session token bloat.

moeragmcp

Disclosure first: I'm the author. MIT, runs locally, zero telemetry.

ragcursormcp+1

Microsoft just patched 137 vulnerabilities across Azure, Windows, Dynamics 365, Copilot, Office, and other products. Most of it looks like the usual Patch Tuesday flood, but one detail stood out: Azure AI Foundry is listed among the high-s…

↯ Copilotcopilotrag

Sharing a project I've been building. Not asking for anything in particular - just thought the problem and approach might be interesting to some folks here.

ragchatgpt

Hi' I'm a long time tabletop game master and a rather neophyte programmer(college diploma in programming for video games, no real work experience yet). I have done a 4 hours AWS workshop on building RAG agents during my intership with a st…

ragcursorchatgpt+1

Everyday I see tons of AI generated posts about tricks to build AI agents. Here is one written by a human with experience and typos :) Step 1: Never directly compete with a human Is there a specific job title for sth?

rag

Merlin Community Local-first dedup for LLM context. Lite engine, MIT integrations, papers on arXiv.

rag

Quick update — my AI Agent Frameworks repo just passed 540+ stars and 60+ forks on GitHub!! When I first put it together, my goal was simple: make experimenting with Agentic AI more practical and approachable.

ragmcpagentic

$ docker run --gpus all -p 8091:8091 codynamics/latch:latest [latch] runtime starting on http://0.0.0.0:8091 [latch] status=loading profile=cdlac_latch_qwen14b_locked_20260317 [latch] warmup complete status=ready $ curl -s http://127.0.0.1…

rag

After shipping AI agents into real production environments, the failures that actually kept us up at night weren't hallucinations or bad outputs — they were control failures. Three things that surprised us: 1.

↯ Securityprompt-injectionragsecurity

FlowFlow Mobile voice notes app with AI chat — 100% Rust, Dioxus iOS, local-first (SQLite + LanceDB). Built with Dioxus 0.7 for iOS.

rag

Hey everyone, I’ve been a developer for a while, but I’ve always been a "lurker" when it comes to open source. Recently, I finally pushed my first project to GitHub: Legax.

rag

Hello guys, me and my team have been building an agentic workflow to answer customer questions (rn in langgraph). The use case goal is to answer ALL customer support questions.

ragagentic

1. The Frontier Giants • Gemini: Access 1.5B tokens/day on Gemini 1.5 Flash/Pro.

↯ Llama↯ Mistralmistralgrokllama+4

A year ago, when I first got into LLMs, I started by using them to play D&D. ChatGPT 4o was surprisingly good at narration, improvisation, and keeping the game moving.

ragchatgptagentic

Working on a support agent and hit a gap I hadn't thought about. Agent completes successfully.

rag

nexa-gauge - Graph-Based Evaluation for LLM and RAG Systems A cache-aware evaluation engine for measuring LLM and RAG output quality with repeatable metrics, cost estimates, and structured reports. Read the Documentation · Quickstart · CLI…

rag

I run ops for a custom home builder. We have SOPs, HR policies, project checklists, and process docs...all living in Dropbox & I want to give my team a simple way to ask questions & get accurate answers without hunting through folders.

raganthropic

we've added onboarding that lets our users build custom AI for their website ready to deploy under 60s total. the platform consists end to end AI engineering, prompts, version control, evaluations, test cases, logs, AI Actions (custom tool…

rag

To iteratively improve RAG performance, current evaluation solutions still take lots of manually work or lots of coding. And it requires close collaboration between AI engineers and domain experts (who may not know how to code).

rag

Hey IH, Like many of you, I’ve been watching traditional SEO traffic drop as Perplexity, SearchGPT, and Gemini Overviews take over. In 2026, if your content isn't being cited, it’s basically invisible.

raggemini

PageIndex: Vectorless, Reasoning-based RAG Reasoning-based RAG ◦ No Vector DB ◦ No Chunking ◦ Human-like Retrieval 🌐 Homepage • 🖥️ Chat Platform • 🔌 MCP & API • 📖 Docs • 💬 Discord • ✉️ Contact 📢 Updates 🔥 Agentic Vectorless RAG — A simple…

ragmcpagentic

The RAG era is ending for agentic AI — a new compilation-stage knowledge layer is what comes next | VentureBeat Orchestration Infrastructure Data Security More Newsletters Featured The RAG era is ending for agentic AI — a new compilation-s…

ragagentic

Local-first second brain with semantic search. Gives Claude persistent memory across conversations — all data stays on your machine.

ragmcp

In this article, you will learn what agentic RAG is, how it differs from traditional RAG, and when to use it. Topics we will cover include: The key limitations of traditional RAG pipelines and what agents add to address them.

ragagentic

For the past two weeks, I’ve been spending 4–5 hours a day building a custom MCP (Model Context Protocol) orchestration server. What started as a simple experiment with Qwen 3.6 35B has evolved into a full-scale "Man-in-the-Middle" proxy t…

↯ Model Context Protocol↯ Qwen 3.6model-context-protocolragqwen+1

How good is Gemini Embedding 001 for scientific retrieval (RAG application)? How does it compare against Text Embedding 3 Large?

raggemini

Honestly, chunking is where a lot of RAG systems start lying to you while still looking fine in the demo. It works when the question is narrow and the document is basically prose, but once users ask messy real questions, the retrieval laye…

rag

Looking for the people actually pushing on multi-agent architectures right now, not the N8N crowd. The progression I've been following: single chat → Claude Code → multi-file projects with context engineering → multi-agent systems → orches…

ragclaude-code

Most agentic-commerce demos I see online are a single agent plus RAG over a product catalog. That shape works for a 200-SKU demo.

ragagentic

Build AI Agents and RAG Applications with the New LangChain + LangGraph Connector for Azure Cosmos DB Building AI agents and RAG applications today means stitching together half a dozen services, a vector database, a chat history store, a…

vector-databaserag

I built EGA - a runtime enforcement layer for LLM outputs. The problem: eval tools score after the fact that something went wrong.

rag

Evaluating RAG feels easy in theory, but production is a different challenge. We’ve been looking into why RAG benchmarking is such a moving target.

rag

I just interviewed Michael Maximilien, former CTO at IBM and Chairperson of NodeJS Foundation, who spent a year shipping production RAG to multiple customers. His lesson was uncomfortable.

vector-databaseragopenai

Persistent AI memory is often reduced to a retrieval problem: store prior interactions as text, embed them, and ask the model to recover relevant context later. This design is useful for thematic recall, but it is mismatched to the kinds o…

rag

Hey guys! I know this is not fully LLM related (its still local though :D), mods feel free to delete this if you think its off topic, but I just wanted to share something I experimented with, local autocomplete without the use of LLMs or f…

rag

To preface this, I work in the oil field, I like to homelab as a hobby. But there is a lot of standards and policies that aren't always easy to find and look up.

openclawrag

I'm trying to build a reliable local RAG setup for claude and it is just exhausting. I want claude to have access to my github repos and past project docs without me copy-pasting everything into the window every morning.

vector-databaseragmcp

I used to be all-in on cloud APIs. For any side project, I’d just grab an OpenAI or Anthropic key and not think twice.

ragopenaianthropic

AI agents are capable enough to automate real work now. But they keep failing because they don't know how a specific company actually operates.

rag

Hey HN, We wanted to share a new tool we’ve been working on. Even when documentation is well-structured, sometimes it’s hard to find what you need.

ragmcp

Hey Everyone, Over the last few months, I noticed a massive gap in how we learn about Agentic AI. There are a million theoretical blog posts and dense whitepapers on RAG, tool calling, and swarms, but almost nowhere to just sit down, run a…

↯ Fine Tuning↯ Function Callingfunction-callingfine-tuningrag+3

Hey all, so I am currently exploring and playing around with Karpathy's LLM Wiki using Claude Code with Ollama and other routed models. I want to create some agents and provide them with tools/plugins, libraries, MCPs, or harnesses to assi…

↯ Ollamaollamaragclaude-code

Hey folks 👋 I’ve been working on an AI agent platform called Noevex, focused on real production use—not just demos. In practice, AI systems struggle with: multi-step orchestration connecting multiple data sources controlling agent actions…

ragagentic

Look, when those 2 million-token context windows dropped earlier this year, I thought RAG was dead. I was like, “Why am I still chunking documents and building vector databases when I can just throw 50 PDFs into one prompt and be done?” So…

rag

LongTerMemory: Technical Overview LongTerMemory is an AI-powered SaaS platform for exam preparation and long-term knowledge retention. It combines Retrieval-Augmented Generation (RAG) with spaced repetition scheduling to help users study s…

rag

Hey Everyone, Over the last few months, I noticed a massive gap in how we learn about Agentic AI. There are a million theoretical blog posts and dense whitepapers on RAG, tool calling, and swarms, but almost nowhere to just sit down, run a…

↯ Fine Tuning↯ Function Callingfunction-callingfine-tuningrag+3

The corpus is Lilian Weng's "LLM Powered Autonomous Agents" — the blog post that the LangChain RAG tutorial uses as its canonical demo. The retriever is the LangChain default (cosine similarity over all-MiniLM-L6-v2 embeddings, top-5).

rag

I spent weeks tuning retrieval models, then realized the real problem was getting sources into clean, structured, interlinked form. Scrape a webpage and you get a mess of HTML.

rag

I keep hitting the same failure mode with agentic RAG over collections of similar PDFs, like monthly electricity and gas bills from the same utility provider. It works well for retrieval: “Find my gas bill from January.” Though even there…

ragagentic

i’ve been experimenting with building a fully local rag pipeline: weaviate for vectors + hybrid search, node.js scripts, qwen 3.5 on ollama what i found is that most of the challenges live in retrieval and chunking, not the LLM, and a good…

↯ Ollama↯ Qwen 3.5ollamaragqwen

The voice guidance layer in our hackathon project uses a Gemini agent backed by a GraphRAG index over the JRCALC 2022 guidelines (the UK ambulance service clinical reference). When the system detects stroke signs or abnormal heart rate it…

raggemini

9 min read Mar 31, 2024 -- After my latest post about how to build your own RAG and run it locally. Today, we’re taking it a step further by not only implementing the conversational abilities of large language models but also adding listen…

↯ Ollamaollamarag

Most RAG setups fail because they treat memory like a static filing cabinet. When every transient bug fix or abandoned rule is stored forever, the context window eventually chokes on noise, spiking token costs and degrading the agent's rea…

rag

I ran a business for 12+ years. Traveling constantly.

ragagentic

Enterprise systems often avoid "monolithic" AI to prevent context rot and hallucinations. The standard fix is task-decoupling: splitting logic between specialized agents or deterministic code.

rag

Wraith Academy is a free hands-on AI pentest curriculum — CTF challenges against live LLM agents covering prompt injection, tool abuse, data exfiltration, RAG poisoning, and more. Earn your WCAP certification.

↯ Securityprompt-injectionragsecurity

Your RAG Pipeline Is Leaking Customer Data Into Vector Embeddings If you're building a RAG (Retrieval Augmented Generation) system on internal documents such as customer support history, knowledge base articles, or internal comms, there's…

retrieval-augmentedrag

My notes live in Obsidian. My reading and highlights live in Readwise.

vector-databaseraggemini+1

What models/quants have impressed you lately for 5060ti ? The use case is professional writing, RAG and long document summarization, not coding, so good instruction following and precision are a plus.

rag

Introduction In the previous post, you saw how you can use tools to add information to an LLM query. In this post, we’ll see another method of adding information to an LLM called RAG, or Retrieval-Augmented Generation.

↯ Securityragsecurity

could not extract summary

rag

👾 Nedster CLI Coding Agent An unstoppable, fully local, open-source coding agent that runs on your consumer GPU. Tags: ollama coding-agent local-ai cli rag chromadb python qwen Are you trying to use local LLMs to autonomously write code, r…

↯ Ollamaollamaragqwen

I’m a data scientist and I find it hard to use Claude Code for SQL - it doesn’t have DB context. so I made yet another database MCP server!

ragmcpclaude-code

ragagentic

↯ Qwen 3.6rag

rag

Steno Compressed memory notation with RAG retrieval for AI agents. Steno solves the AI memory problem: agents accumulate knowledge across sessions, but loading everything into context every time is expensive, noisy, and causes drift.

rag

It lets you search 1M+ jobs across multiple regions, refine by keyword/category/location, upload a plain text resume for better matching, filter by target companies. Searches, keywords extracted from resume and bookmarks are saved locally,…

rag

I’m currently preparing for AI-focused roles and would love to get perspectives from people already working in the industry. For context — I have ~5 years of experience as a Full Stack Engineer with a strong focus on AI systems.

↯ Llama↯ Llama 3.3llamaragagentic

Scenario A: Given: A search query to reduce context is provided When: Results are pushed to the system as completion. Then: a question will respond with accurte results Scenario B: Given: Scenario A data is in a slots KV Cache When: new se…

rag

rag-workshop A local-first RAG system built autonomously by a multi-agent framework. This repository is the reference implementation produced by the C.E.H.

rag

Advanced GPU infrastructure, collaborative AI Agents, and intelligent RAG systems. Build, deploy, and scale AI solutions with comprehensive tools.

↯ Opus 4.7ragopus

I need to create an ecommerce shopping assitant chatbot. Customers would reach out via chat, and the agent/chatbot would help check inventory and make product recommendations based on what customers share.

rag

every other day someone here posts about karpathy's llm wiki idea, or "how do I give my agent context about me," or "I want a personal knowledge base my AI can use." and then the comments are always the same - build RAG, write a pipeline,…

ragmcp

I'm looking for suggestions for an agent harness that uses a database (SQLlite, RAG, what ever) for long-term context. I plan to use my RTX3080 & 3090 for local AI, though I expect to use APIs for some tasks.

rag

Converting spreadsheets to graph databases (Neo4j, Neptune, etc.) usually means manually defining nodes, relationships, and writing Cypher from scratch. It's tedious.

rag

How to diagnose RAG failures from traces If a RAG system fails in production, the first question we should be asking is "what broke in this trace?". Until you can answer that, most scorers or dashboards aren't going to help you.

rag

Hi all, I developed an addition on a CRAG (Clustered RAG) framework that uses LLM-guided cluster-aware retrieval. Standard RAG retrieves the top-K most similar documents from the entire corpus using cosine similarity.

rag

I'm developing a specialized Red Team audit framework focused on real-world AI agent and RAG security risks (prompt injection, tool misuse, excessive agency, indirect injection through documents, memory poisoning, etc.). I’m looking for a…

↯ Securityred-teamprompt-injectionrag+1

Redirecting from /blog/llm-wiki-needs-a-substrate/ to /blog/rip-rag

rag

Fixed-size chunking splits text at arbitrary token boundaries, cutting mid-sentence and blending unrelated topics into the same chunk. Here’s how to build a two-stage pipeline with LlamaIndex , structural splitting first, semantic coherenc…

rag

Hello! I’m working on RAG system on arabic documents any idea on the best embedding model out there?

rag

Been seeing this idea where instead of doing the usual RAG loop, you compile all your sources into a markdown wiki first, then query that directly. The interesting part is that saved answers become part of the wiki too.

rag

atlas-detect MITRE ATLAS technique detection for LLM and AI agent security. Detects 97 attack techniques across 16 MITRE ATLAS tactics including prompt injection, jailbreaks, credential exfiltration, model extraction, RAG poisoning, revers…

↯ Securityprompt-injectionragsecurity

Recently got recruited tin PwC post masters in data science. Interview was in traditional ml but now I must work in AI projects.

rag

Graph-based retrieval-augmented generation (RAG) is widely used for multimodal, cross-document question answering. However, building corpus-level graphs is expensive, slow to query, and difficult to maintain.

rag

Modern applications generate massive volumes of raw telemetry data, but translating those noisy, heterogeneous event streams into actionable business insights remains a fundamental challenge. Data engineers and analysts expend substantial…

rag

Effective troubleshooting agents in enterprise customer support depend on retrieving actionable guidance from similar historical cases, yet existing retrieval-augmented generation (RAG) systems treat support cases as static documents and o…

rag

Deploying retrieval-augmented generation (RAG) on commodity GPUs such as the NVIDIA T4 (16 GB VRAM) exposes a practical failure mode we call the Compression Paradox: neural prompt compression can add key-value (KV) cache contention and pre…

rag

We introduce MiRAGE, an evaluation framework for retrieval-augmented generation (RAG) from multimodal sources. As audiovisual media becomes a more prevalent source of information online, RAG systems must integrate such media into generatio…

retrieval-augmentedrag

Retrieval-Augmented Generation (RAG) systems typically employ fixed retriever and generator configurations across queries, despite substantial differences in query complexity and information needs, leading to inefficient allocation of comp…

rag

Hi r/ClaudeAI, PowerChat is a free iOS client that can connect your cloud / local models to MCP servers. It has builtin support for Google Drive, iOS Files, iOS Calendar, and the search engines Tavily, Exa, and Firecrawl.

ragmcp

Scientific research increasingly relies on large, heterogeneous data sources, motivating interest in retrieval-augmented generation (RAG) systems that provide natural language access to scientific knowledge and research workflows. Research…

rag

Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for improving the quality of generated contents of Large Language Models (LLMs) by grounding responses in external knowledge, thus reducing hallucinations and factual…

rag

Retrieval-Augmented Generation (RAG) serves as the primary memory architecture for long-horizon autonomous agents. However, treating shared memory as an append-only stream introduces \textit{Semantic Shadowing}, a critical failure mode whe…

rag

Accessing legal information in Nepal is difficult due to complex terminology, limited resources, and misinformation. We introduce an AI-powered legal assistant that is tailored for Nepali legal texts and is built on a fine-tuned large lang…

rag

Retrieval-Guided Fine-Tuning (RAG-FT) incorporates retrieved data directly into the training objective, but the statistical consequences of noisy retrieval during training remain theoretically undercharacterized. We study this question by…

↯ Fine Tuningfine-tuningrag

Retrieval-augmented generation (RAG) can improve access to complex information; however, retrieving evidence alone does not ensure that answers are grounded, citation-valid, or appropriately refused. This paper introduces CiteGuard-RAG, a…

rag

Although existing multi-agent Retrieval-Augmented Generation (RAG) systems have demonstrated promise on complex multimodal reasoning tasks, they remain fundamentally limited in reasoning depth and memory structure, suffering from inadequat…

rag

AI-powered gameplay support agents hold promise for game-based learning, yet grounding generative models in structured game data remains an open challenge. We present PEARL (Parallel Education Agent for Reflection and Learning), a dual-com…

rag

Graph-RAG systems often assume pristine data quality, overlooking the severe impact of perturbations in cloud-native databases. This paper proposes a three-layer decoupled diagnostic framework to orthogonally attribute system errors to rea…

rag

Knowledge-intensive analytical applications retrieve context from both structured tabular data and unstructured, text-free documents for effective decision-making. Large language models (LLMs) have made it significantly easier to prototype…

rag

Visual Retrieval-Augmented Generation (VRAG) empowers models to navigate and answer queries about visually rich documents by retrieving relevant page images as visual evidence and reasoning over their content. However, effectively utilizin…

ragagentic

Adaptive RAG often uses retrieval-time signals to decide whether another retrieval, reranking, or multimodal step should run. We ask whether these signals add routing value once the query itself is already known.

rag

As enterprises modernize legacy monolithic systems to microservices, Large Language Models (LLMs) are heavily utilized for automated code translation. However, traditional vector-based Retrieval-Augmented Generation (Standard RAG) struggle…

rag

Novice math teachers often encounter students' mistakes that are difficult to diagnose and remediate. Misconceptions are especially challenging because teachers must explain what went wrong and how to solve them.

rag

Retrieval-augmented generation (RAG) improves knowledge-intensive large language model (LLM) applications by conditioning generation on retrieved documents, but longer contexts increase latency, key-value (KV) cache memory, and token cost.…

rag

Allowing large language models (LLMs) to retrieve information from a set of trusted documents can increase reliability and reduce hallucination. However, recent work has demonstrated that retrieval-augmented generation (RAG) can have unint…

↯ Hallucinationhallucinationrag

In Retrieval-Augmented Generation (RAG) systems, a large number of retrieved chunks are concatenated to form the input context so that users can receive high-quality responses based on external knowledge. As a result, the input context len…

↯ Fine Tuningfine-tuningrag

Retrieval-augmented generation (RAG) grounds a language model in retrieved documents, which reduces hallucination but creates a new attack surface: if retrieved text is tampered with, the model may repeat the falsehood. We study how much a…

↯ Hallucinationhallucinationrag

Retrieval-Augmented Generation (RAG) equips large language models with external knowledge and is central to knowledge-intensive tasks. As RAG systems enter real-world use, generators must reliably leverage retrieved evidence.

rag

Evaluating first-stage retrievers in large-scale production RAG requires a benchmark that pairs a large-scale corpus with a large set of agent-reformulated search queries based on real user queries and their conversation threads, and that…

ragagentic

Retrieval-augmented multi-hop question answering (QA) decomposes a query into sub-questions and decomposes the corpus into smaller retrieval units such as sentences. Both forms of decomposition improve the pipeline, but we show that both s…

rag

A popular way to improve Retrieval-Augmented Generation (RAG) is to rewrite the user's question into several variants and search with all of them. We test whether this actually helps once the underlying search is already strong.

rag

Many real-world questions over business, legal, and scientific corpora are natural-language versions of database-style queries over records latent in text. Existing retrieval-augmented generation (RAG) systems are optimized primarily for s…

operatorrag

Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by incorporating external knowledge, particularly for long-tail domains such as literary works. However, the critical step of document segmentation in RAG remains l…

rag

Despite the strong capabilities of multimodal large language models (MLLMs), their parametric knowledge remains incomplete and difficult to update, motivating multimodal retrieval-augmented generation (RAG) to ground responses in external…

rag

Vision-language models (VLMs) augmented with retrieval-augmented generation (RAG) benefit from access to external evidence. However, standard retrievers and rerankers optimize for semantic similarity rather than answer utility, creating a…

rag

Goal-oriented conversational systems must answer factual questions, understand visitor-provided information, and advance business objectives without becoming rigid questionnaires. This paper proposes a Symbolic-RAG-Generative architecture…

rag

Graph-agentic retrieval-augmented generation combines structured evidence with adaptive controllers that can plan retrieval, traverse relations, verify intermediate claims, delegate subtasks, and use tools. This combination is useful when…

ragagentic

Retrieval-Augmented Generation (RAG) enhances language models with external knowledge, but the lengthy retrieved context inflates the input and degrades inference efficiency. Soft context compression encodes each document into a substantia…

rag

Large language models deployed in high-stakes settings frequently generate plausible but ungrounded claims. Standard retrieval-augmented generation (RAG) pipelines offer limited remedy, since they retrieve isolated passages without trackin…

↯ Copilotcopilotrag

Recent structured RAG methods leverage tree- or graph-based reasoning structures to improve multi-hop QA. However, they face key limitations in evidence-intensive QA, where answering a question requires synthesizing information scattered a…

rag

We’ve been testing a provenance-heavy RAG/knowledge system on real cases, and two areas are now hard to validate simply because our current corpus doesn’t contain enough of them: Documents that change over time — policies, specs, manuals,…

rag

Retrieval-Augmented Generation (RAG) has made dense retrieval over large document collections a standard building block. Organizations increasingly outsource vector indexes to untrusted clouds, exposing proprietary corpora and user queries.

rag

Large language models (LLMs) have transformed natural language processing (NLP), enabling diverse applications by integrating large-scale pre-trained knowledge. However, their static knowledge limits dynamic reasoning over external informa…

rag

Third-party retrieval-augmented generation (RAG) marketplaces create a new auditing problem: data providers may license corpora to a RAG operator, yet later have no visibility into whether their documents are being reused without compensat…

operatorrag

Retrieval-Augmented Generation (RAG) has become a prevailing paradigm for enhancing Large Language Models (LLMs) with non-parametric knowledge. Vanilla RAG efficiently handles simple queries but struggles with relational or multi-hop reaso…

rag

Retrieval Augmented Generation (RAG) is a key component for generating accurate and hallucination free answers using Large Language Models (LLMs). LLMs are improving at handling long context, but still suffer from "lost in the middle" prob…

↯ Hallucinationretrieval-augmentedhallucinationrag

Selecting a retrieval model for a production RAG system requires reliable comparative evaluation, but obtaining relevance judgments at scale is expensive and difficult to repeat as new candidate systems arrive. We study pooled LLM evaluati…

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In customer contact centers, human agents often struggle with long average handling times (AHT) due to the need to manually interpret queries and retrieve relevant knowledge base (KB) articles. While retrieval augmented generation (RAG) sy…

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Retrieval-Augmented Generation (RAG) mitigates large language models (LLMs) hallucinations, yet conventional dense retrieval struggles with the complex reasoning paths of multi-hop question answering (QA). Graph-based RAG captures multi-st…

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A retrieved document contains a planted instruction. The Agent repeats it and cites the source, but doesn’t actually execute anything.

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I’m trying to understand the best practice for using Claude to read and analyze PDFs in a normal chat use case, rather than building a complex RAG or production pipeline. The reason I’m asking is that we’ve been receiving frequent complain…

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I'm sharing a RAG and desktop document review system I made, mostly with Claude. It's called Fact Extract.

ragmcp

Retrieval-Augmented Generation (RAG) improves the factuality of large language models (LLMs), yet existing RAG systems often struggle with complex, multi-step reasoning that requires adaptive retrieval and continuous revision of intermedia…

ragagentic

Multi-hop question answering in retrieval-augmented gener?ation (RAG) often benefits from retrieving beyond the few candidates that will finally be read: narrow retrieval can miss an indispensable hop, while expanded retrieval introduces t…

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Attention is the dominant source of latency during long-context LLM inference, an increasingly popular workload with reasoning models and RAG. We propose Kascade, a training-free sparse attention method that leverages known observations su…

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Retrieval-Augmented Generation (RAG) has become an effective method for enhancing large language models (LLMs) with up-to-date knowledge. However, it may pose a risk of copyright infringement, as IP datasets may be incorporated into the kn…

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Deploying large language models for legal question answering raises challenges that general-purpose leaderboards do not capture, particularly for low-resource languages and under hard operational constraints. We report on building and oper…

↯ Fine Tuningfine-tuningrag

Low-resource languages without an adequate training corpus often use a related, higher-resource language as a scaffold for comprehension. Still, there is a need to develop rigorous evaluation methods to identify when models fail in cross l…

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Accurate interpretation of single-visit and longitudinal clinical assessments for Parkinson's disease is time-consuming and often depends on specialist expertise. Although large language models (LLMs) can generate natural language summarie…

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Federated Retrieval-Augmented Generation (FedRAG) is attractive for privacy-sensitive applications because full local corpora remain on clients. As a result, routing must rely on client-provided semantic profiles, creating a new opportunit…

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In Retrieval-Augmented Generation (RAG), retrieval may provide insufficient or conflicting information needed to answer a question. The system should not only know when to answer but also be able to identify cases in which the documents pr…

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Fallacies are arguments that employ invalid reasoning, making their automatic detection critical in sensitive contexts such as high-stakes political debates, where public opinion is shaped. Spotting a fallacious argument requires contextua…

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Retrieval evaluation for retrieval-augmented generation (RAG) is increasingly designed around whether retrieved passages contain evidence that can support generation, rather than topical relevance alone. We study whether this closer alignm…

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Expert-level financial question answering requires both grounded verification to catch numeric hallucinations and audit trails for regulatory compliance, attributes that standard single-pass RAG systems lack. We take a step toward this goa…

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How documents are segmented into retrievable chunks and how those chunks are embedded strongly affect Retrieval-Augmented Generation (RAG) quality, yet neither has been systematically studied for morphologically rich languages such as Turk…

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With the lack of support from Qwen regarding the smaller 9B and 35B MOE models. Like myself, not everyone is looking for an agentic coding model, I particularly use it for RAG and reviewing and require high reasoning across different docum…

moeragqwen+1

Retrieval Augmented Generation (RAG) is a highly effective paradigm for keeping LLM-based responses up-to-date and reducing the likelihood of hallucinations. Yet, RAG was recently shown to be quite vulnerable to corpus knowledge poisoning:…

retrieval-augmentedrag

The functional annotation of genes in non-model organisms remains a significant challenge in computational biology, with 20-70% of sequenced genes lacking characterized functions. Traditional homology-based methods are often costly and str…

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Retrieval-augmented generation (RAG) is widely used to mitigate hallucination issues in large language models (LLMs) and multimodal large language models (MLLMs). In particular, knowledge graph (KG)-based RAG leverages structured knowledge…

↯ Hallucinationhallucinationrag

Graph-based RAG improves multi-hop question answering by organizing evidence as a knowledge graph. However, most existing RAG systems process each query in isolation and discard useful reasoning from the LLM's response after inference.

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Graph-based retrieval-augmented generation connects facts that no single passage states, but current implementations pay for that three times: in infrastructure, requiring a vector store, graph database and document store to be kept consis…

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Retrieval-Augmented Generation (RAG) grounds LLM responses in external evidence but treats the model as a passive consumer of search results, with no view of how the corpus is organized or what it has not yet seen. We present Corpus2Skill,…

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Civil infrastructure compliance checking has long relied on engineers manually reading legacy 2D plans; however, OCR-based automation strips away the geometry and layout essential for interpreting these plans. We present a Visual-First Mul…

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Long Narrative Reasoning is an essential capability for processing and reasoning over complex narratives. While retrieval-augmented generation provides a promising framework, existing methods still face two critical challenges: cognitive i…

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Retrieval-augmented generation (RAG) is widely assumed to mitigate factual errors in large language models (LLMs), but it remains unclear whether retrieval uniformly compensates for missing knowledge. We study this question in a controlled…

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Retrieval-augmented generation (RAG) improves large language models by incorporating external knowledge without retraining, but existing methods often underuse the relational structure encoded in knowledge graphs. Graph-based RAG can captu…

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Existing methods for improving Retrieval-Augmented Generation (RAG) efficiency mainly optimize downstream LLM generation, such as context compression or serving optimization. However, RAG is an end-to-end system, and its bottleneck can shi…

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I ended up fine tuning a mistral 7b to outperform our costly foundational model and saved $300k. I previously thought that fine tuning was pointless (it's definitely not) and that all these problems could be solved with RAG (they can't).

↯ Mistralmistralrag

I was bored and handwrote a tiny 100-line bash script to let an agent search for and read articles from an offline wikipedia archive during a regular chat. It's not particularly useful, but it's definitely neat and a big step up from llama…

↯ Llamallamaragqwen+1

Four months ago, I shared google-surf-mcp here as a lightweight MCP for browser-based Google search and URL extraction without API keys. I got tired of AI agents forgetting previous research and discarding context between sessions, so I ev…

ragmcp

Benchmarked qwen3.8 xhigh, medium and muse glimmer. Xhigh effort mode with qwen3.8 took almost 30hrs.

↯ Qwen 3.8ragqwen

Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems are increasingly integrated into clinical workflows. However, prompt injection attacks can steer these systems toward clinically unsafe or misleading outputs.

↯ Securityprompt-injectionragsecurity

Evaluating Retrieval-Augmented Generation (RAG) systems requires assessing not only end-to-end correctness but also how individual components interact and how errors propagate through the pipeline. We introduce a Bayesian evaluation framew…

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Agentic retrieval-augmented generation (RAG) requires language models to decide when to continue searching and when to answer. Existing RL-based methods rely on external supervision and overlook the agent's internal belief about whether th…

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Memory and RAG evaluations often treat the answering model's input as an implementation detail, even though systems may render the same history as a memory entry, summary, typed record, or raw excerpt. We introduce RENDER, a benchmark cont…

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RAG systems are increasingly used to summarize what large collections of documents say. A user asks "What do people think about X?" and receives an answer that reads as consensus.

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Retrieval-Augmented Generation (RAG) enables large language models to incorporate external knowledge during generation, improving factual grounding and domain adaptability. However, existing RAG pipelines assume that evidence retrieved fro…

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Speech-based applications pass spoken queries through automatic speech recognition (ASR) before any retrieval module, so ASR errors enter the pipeline as a fixed upstream constraint. We empirically test whether two extensions to standard r…

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Retrieval-augmented generation (RAG) enables LLMs to access external knowledge for answering knowledge-intensive questions. For complex multi-hop questions, multi-turn retrieval-augmented reasoning extends RAG into an iterative process tha…

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LLM responses are based on the internet (via training or RAG), and AI is now used to generate a significant amount of content online (Paredes et al., 2026), creating the potential for a self-reinforcing feedback loop. Prior work has shown…

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Multimodal Retrieval-Augmented Generation (RAG) with visual citation is crucial for ensuring the traceability and verifiability of MLLMs. However, current RAG and SFT-based methods struggle to achieve robust cross-modal reasoning, causing…

ragagentic

Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for combining large language models (LLMs) with external knowledge sources. However, RAG systems remain vulnerable to prompt injection attacks, which may mislead the r…

↯ Securityprompt-injectionragsecurity

Retrieval-augmented generation (RAG) is typically optimized for topical relevance, yet its success ultimately depends on whether retrieved passages are useful for a large language model (LLM) to generate correct and complete answers. We ar…

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We introduce AI University (AI-U), a flexible framework for AI-driven course content delivery that adapts to a course's instructional style. AI-U combines a fine-tuned large language model (LLM) with retrieval-augmented generation (RAG) an…

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Graph-based retrieval-augmented generation (RAG) provides a scalable paradigm for long-video understanding, but existing systems typically inherit a fixed temporal granularity from video segmentation when constructing their retrieval index…

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Current work on improving reliability in large language model (LLM)- generated answers has primarily leveraged Retrieval-Augmented Generation (RAG), knowledge-graph augmentation, and reinforcement learning. While these methods are adept at…

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Recent advances in LLMs and the adoption of RAG systems in industry have created a need for domain-specific question-answer datasets that can assess RAG performance on proprietary data. Existing datasets, such as HotpotQA, challenge curren…

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Retrieval-Augmented Generation (RAG) grounds LLM generation on retrieved documents, but the standard terminal retrieval stage--dense-vector similarity, optionally followed by reranking--often returns documents that share keywords with the…

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Heterogeneous agentic retrieval-augmented generation (RAG) systems increasingly orchestrate external APIs, internal databases, vector stores, and graph stores. Exposing all tool descriptions to an LLM agent, or selecting tools only by vect…

ragagentic

We ask whether stereotype-loaded queries about culturally marked people leak more personal information from a retrieval-augmented generation (RAG) system than otherwise-equivalent neutral queries. We pre-register a four-culture audit (en-A…

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Retrieval-Augmented Generation (RAG) has significantly improved the performance of large language models (LLMs) on knowledge-intensive tasks in recent years. However, since retrieval systems may return irrelevant content, incorporating suc…

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Question answering (QA) over long, connected documents remains challenging because relevant evidence may span multiple entities and their relationships. Existing retrieval-augmented generation (RAG) methods typically index documents as raw…

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Retrieval-Augmented Generation (RAG) grounds Large Language Model (LLM) outputs in external knowledge, but RAG systems usually trust whatever they retrieve, creating a Security-Reliability Gap: high semantic relevance does not guarantee fa…

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Agentic retrieval-augmented generation (RAG) interleaves retrieval, reasoning, and answer generation across multiple hops. A retrieval error at hop 1 can surface only as a wrong answer at hop 3, while later retrieval can also repair the tr…

ragagentic

Nearly every retrieval-augmented question-answering system in production ships with a hidden interpreter: on each query a language model re-derives the meaning of raw corpus text and then throws that work away. Cheaper models do not close…

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As I’m navigating the best setup for using Qwen3.8 27b as both my main coder and my personal assistant, I want to hook it into Wiki & beyond but I don’t want to be reliant on an internet connection. What I’ve started doing is taking the ge…

↯ Qwen 3.8ragmcp

I was debugging a customer support agent that kept getting stuck in recursive tool-call loops (e.g., retrying the same failed SQL query 15 times before hitting the max iteration cap), and I realized how brutal the underlying math is. Becau…

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Retrieval-augmented generation (RAG) improves language-model responses by grounding generation in external passages, which comes with overhead: retrieved context lengthens the prompt, increasing prefill work, KV-cache footprint, memory tra…

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repo: https://github.com/infino-ai/code-context install: /plugin marketplace add infino-ai/code-context /plugin install code-context@infino-ai The problem it fixes: Claude code finds things by grepping and reading files. grep is great...

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This paper addresses end-to-end geospatial question answering over multilingual toponymic data. We introduce a bilingual (Russian-Tatar) dataset of 9,688 toponyms with linguistic, etymological, and coordinate information (93.1 percent geor…

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Knowledge graph question answering (KGQA) and knowledge-graph-based retrieval-augmented generation (KG-RAG) aim to ground answers in explicit graph evidence, but real-world knowledge graphs are often sparse, outdated, and incomplete. Exist…

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Large vision-language models perform well on short- and medium-length video understanding but still struggle to maintain coherent event memory and recover long-range relationships in ultra-long videos. End-to-end methods are limited by vis…

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I am working on building a chatbot for an online store. I will be using MCP for the transactional parts including product search, adding to cart, etc.

ragmcp

Retrieval-Augmented Generation (RAG) has significantly enhanced the performance of large language models (LLMs), yet these systems remain vulnerable to knowledge-poisoning attacks, in which misinformation in retrieved documents can influen…

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Retrieval-Augmented Generation (RAG) fine-tuning has shown substantial improvements over vanilla RAG, yet most studies target document question answering, leaving open whether these gains transfer to specialized tasks. We study supervised…

↯ Fine Tuningfine-tuningrag

When consequential decisions depend on knowledge that exists nowhere in writing, LLMs hallucinate not from retrieval failure but from model absence. RAG and knowledge-graph methods share a structural ceiling.

↯ Hallucinationhallucinationrag

Legal consultation questions exhibit multi-level complexity. A single retrieval strategy often leads to over-reasoning for simple questions and poor interpretability for complex ones, making it difficult to meet the requirements for both a…

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We preregistered a comparison of two ways to help an LLM answer questions over a small research corpus: a single-round Vector RAG system and an LLM-compiled markdown wiki browsed by a tool-using agent. Both systems answered the same 13 que…

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Multimodal Retrieval Augmented Generation (M-RAG) is increasingly vulnerable to adversarial attacks where malicious data are crafted to produce embeddings that align with benign entries in the vector space, deceiving retrieval and inducing…

retrieval-augmentedrag

Retrieval quality in RAG systems is commonly reported as a single aggregate score, which can hide large differences across query types and language conditions. We study this problem in Bengali agricultural advisory, where farmer queries ar…

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Recent advances in Large Language Models (LLMs) have driven their adoption in recommender systems through Retrieval-Augmented Generation (RAG) frameworks. However, existing RAG approaches predominantly rely on flat, similarity-based retrie…

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Retrieval-Augmented Generation (RAG) and its Multimodal Retrieval-Augmented Generation (MRAG) significantly improve the knowledge coverage and contextual understanding of Large Language Models (LLMs) by introducing external knowledge sourc…

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Large Language Models (LLMs) are increasingly being deployed in cybersecurity operations to assist cybersecurity analysts with rapid decision-making against emerging threats. However, there is a main criteria that must be met when using LL…

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Retrieval-Augmented Generation over knowledge graphs (Graph-RAG) has emerged as a powerful paradigm for grounding large language models in domain-specific corpora. However, existing systems face persistent limitations: (1) static chunking…

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Multimodal retrieval-augmented generation (RAG) systems often rely on long unstructured contexts or aggressively expanded evidence graphs, which can introduce noisy evidence, weaken multi-hop reasoning, and increase unsupported generation.…

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Been a ChatGPT/Codex user since basically day one, finally giving Claude a real shot, and I ran straight into something I didn't expect. My setup: 8 textbooks (4 biochem, 3 ochem) converted to markdown with figures stripped out.

ragchatgptcodex

The rapid progress of large language models (LLMs) is shifting semantic search toward a question-answering paradigm, where users ask questions and LLMs generate responses. In high-stake domains such as law, retrieval-augmented generation (…

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Large Language Models (LLMs) increasingly rely on multi-turn reasoning and interaction, such as adaptive retrieval-augmented generation (RAG) and ReAct-style agents, to answer difficult questions. These methods improve accuracy by iterativ…

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Hallucination is a major challenge for retrieval-augmented generation (RAG) systems in the legal domain, where ungrounded answers can lead to serious consequences. To better understand this problem, we conduct a fine-grained analysis of ha…

↯ Hallucinationhallucinationrag

Existing multimodal RAG methods often flatten structured documents into isolated text and image units, weakening the source organization and local text-image logic needed for faithful evidence selection and placement. We propose HAM-RAG, a…

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Automatically generating textbook-grounded assessment items can reduce science teachers' workload, but existing retrieval-augmented generation (RAG) systems rely on flat retrieval, support only single-question generation, lack safeguards a…

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Apologies for the meandering human-authorship of my post up front; If this is the wrong sub or I should post elsewhere, I humbly thank you for correcting my mistake. TLDR at the top for those who prefer to skip the novel.

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I kept hitting the same failure mode with Claude Code and Cursor. The agent is good.

ragcursorclaude-code

Parliamentary proceedings are a primary record of democratic deliberation, yet their volume and fragmentation make multi-perspective access difficult for citizens, journalists, and researchers. Applying Retrieval-Augmented Generation (RAG)…

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Retrieval-Augmented Generation (RAG) is widely used to improve the performance of Large Language Models (LLMs) in answering user queries. Existing privacy research on RAG has focused on preventing unauthorized users from accessing sensitiv…

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Hi I'm taking up a new compliance role doing lots of auditing for care homes. The auditing will be of medication, sop's, and other regulation requirements.

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While retrieval-augmented generation (RAG) has proven effective at giving LLMs access to external knowledge, mainstream dense-retrieval implementations remain inherently limited in handling structured constraints and multi-hop reasoning. G…

retrieval-augmentedrag

General-purpose large language models (LLMs) have recently been reported to match or exceed specialized clinical AI tools on medical benchmarks, but such comparisons draw on a narrow set of systems and on benchmarks developed largely in hi…

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Retrieval-Augmented Generation (RAG) repeatedly prefills identical text chunks across queries, incurring redundant computations. Position-Independent Caching (PIC) mitigates it by reusing precomputed Key-Value (KV) across positions, but it…

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Enterprise RAG deployments face a critical reliability gap: while LLMs satisfy 80% of individual constraints, only 26.8% of responses meet all requirements simultaneously, revealing a 57-point orchestration gap. Existing benchmarks assume…

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Retrieval-augmented generation improves the factuality of large language models by grounding responses in retrieved evidence, yet existing evaluation frameworks struggle to provide consistent, fine-grained diagnostics across the diverse sp…

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Graph RAG on corpus graphs enhances retrieval by leveraging intermediate node content as contextual clues to uncover unretrieved oracle nodes. However, existing methods suffer from two critical blind spots, namely semantically blind graph…

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Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising paradigm for training agentic retrieval-augmented generation (RAG) systems from outcome-only supervision. Most existing methods optimize policies from uniform…

ragagentic

A failed retrieval-augmented generation (RAG) answer can be consistent with several unseen responses to evidence repair. We introduce Pair-ID, an offline audit that holds one query, retrieval state, and reader constant, then crosses two op…

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Retrieval-Augmented Generation (RAG) systems in production operate under strict service level objectives (SLOs) on tail latency and infrastructure cost. However, standard retrieval pipelines rely on fixed retrieval budgets that ignore quer…

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Open-vocabulary 3D Scene Graph (3DSG) can enhance various downstream tasks in robotics by leveraging structured semantic representations, yet current 3DSG construction methods suffer from semantic inconsistencies caused by noisy cross-imag…

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Answering complex conditional questions using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) remains a challenge, particularly in domain-specific contexts where general-purpose LLMs and RAG tend to underperform. We h…

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