Part 5 — Installing a Black Box Recorder in Your RAG System: 4-Layer Metadata + 3-Level Verification, Root Cause in 5 Minutes
This article covers the fifth layer of the full-stack architecture: Full-Chain Traceability. This is...
#RAG
1704 articles tagged with RAG
I Shipped a Strict-Source RAG System to Production in 8 Weeks: A Full-Stack Engineering Retrospective
This is a story about "getting RAG right" — not a demo, but a production system under real business...
Choosing a RAG backend for local AI — an opinionated map:🗄 sqlite-vss / sqlite-vec: zero infra, great for single-machine...
Choosing a RAG backend for local AI — an opinionated map:🗄 sqlite-vss / sqlite-vec: zero infra, great for single-machine dev📦 Qdrant: best self-hosted HNSW perf, ships as a single ...
Building Minyut: An Embeddable RAG Chatbot in One Script Tag
A client needed their customers to be able to query a 40-page policy document without reading through...
Stop telling your RAG bot not to hallucinate. Make it impossible.
The suggestion every RAG app ignores If you've shipped a retrieval-augmented assistant,...
日本、ここはシェバトに何とかしてもらいたいものです【完全ローカル】AIに記憶を持たせる5ステップ — Ollama×RAGでつくる長期記憶チャットボット - Qiita https://qiita.com/hatsukaze/items/1...
日本、ここはシェバトに何とかしてもらいたいものです【完全ローカル】AIに記憶を持たせる5ステップ — Ollama×RAGでつくる長期記憶チャットボット - Qiita https://qiita.com/hatsukaze/items/192403c9ff6a433fe0b6#Apple #LLM #news #bot
Building a RAG Pipeline From Scratch: What SmartQueue Taught Me About Retrieval
How I built a retrieval-augmented AI assistant for an IT helpdesk queue, why I ripped out ChromaDB for a from-scratch BM25 implementation, and what the tuning numbers in the pipeli...
A Complete Guide to AI Agents by Samir Solanki is a new release on Leanpub!From LLMs and RAG to Memory, MCP, Agent Frame...
A Complete Guide to AI Agents by Samir Solanki is a new release on Leanpub!From LLMs and RAG to Memory, MCP, Agent Frameworks, and Enterprise AI Controls—discover how modern AI Age...
Building a Production-Ready RAG Application with LangChain, pgvector, and Gemini
Retrieval-Augmented Generation (RAG) is a powerful pattern to build applications that can query,...
SantanderAI/linear-adapter-trainer: Train linear embedding adapters with triplet loss to align retrieval embeddings with your queries (RAG).
Train linear embedding adapters with triplet loss to align retrieval embeddings with your queries (RAG).
Fixing AI Observability: How I Added GenAI Semantic Support for RAG Embedding Spans in Mastra
OpenTelemetry has become the standard for observing modern systems. But when you start building AI...
《打造超人AI學習》：終結AI胡言亂語的「RAG思維」，準確度一舉飆破95%精選書摘 2026-06-17 20:00:00 CST跨界學習大神以認知科學X腦科學X RAG架構論證，讓你的大腦接上AI外掛。上課、閱讀、筆記、研究、技能學習全...
《打造超人AI學習》：終結AI胡言亂語的「RAG思維」，準確度一舉飆破95%精選書摘 2026-06-17 20:00:00 CST跨界學習大神以認知科學X腦科學X RAG架構論證，讓你的大腦接上AI外掛。上課、閱讀、筆記、研究、技能學習全面超速！https://www.thenewslens.com/article/268485#書摘導購 #幻覺 #模型選...
When RAG Gives Wrong Answers: A Debugging Walkthrough
Last week a client pinged me. Their internal AI assistant was confidently telling employees the wrong...
RAG от А до Я: шпаргалка архитектора (векторные базы, чанкинг, реранкинг и 8 граблей продакшена)Создать демо-версию RAG ...
RAG от А до Я: шпаргалка архитектора (векторные базы, чанкинг, реранкинг и 8 граблей продакшена)Создать демо-версию RAG сегодня можно за 15 минут: LangChain, ChromaDB, API OpenAI —...
RAG（検索拡張生成）の実践：AIを活用した文書生成と検索の最適化この記事では、RAG（検索拡張生成）技術の概要とその実践的な活用方法について説明します。RAGは、AIを活用して文書生成と検索を最適化するためのアプローチです。最新の研究と事...
RAG（検索拡張生成）の実践：AIを活用した文書生成と検索の最適化この記事では、RAG（検索拡張生成）技術の概要とその実践的な活用方法について説明します。RAGは、AIを活用して文書生成と検索を最適化するためのアプローチです。最新の研究と事例を紹介しながら、RAGの特徴と効果について詳しく解説します。https://ai-blog-seven-wine.ve...
Configurable Clinical Information Extraction with Agentic RAG: What Works, What Breaks, and Why
Patient contexts span hundreds of heterogeneous documents and thousands of structured data points, yet the document-level metadata that AI systems need for retrieval and triage is ...
SproutRAG: Attention-Guided Tree Search with Progressive Embeddings for Long-Document RAG
Retrieval-augmented generation (RAG) systems must balance retrieval granularity with contextual coherence, a challenge that existing methods address through LLM-guided chunking, si...
🛠️I spent 10 hours last week mapping out a RAG pipeline. Three things became clear:Week 1 of building this from scratch ...
🛠️I spent 10 hours last week mapping out a RAG pipeline. Three things became clear:Week 1 of building this from scratch was intense but rewarding. I wanted to understand how the pi...
Building a Local Knowledge Graph RAG System for News Analysis with Neo4j and Ollama
I created NewsGraphRAG as a personal deep-dive to explore the boundaries of hybrid local retrieval...
From "I Understood Nothing" to Building a RAG App
Yesterday, I took the time to go through 31 pages of my own notes on NLP, notes I had carefully...
Async Scraping Jobs Are Usually a Better Fit for RAG Ingestion Than Blocking Requests
A practical look at async scraping job APIs for RAG pipelines, including polling, retries, stale data, selector drift, and failure modes.
Your RAG Agent Is Retrieving the Wrong Chunk: 5 Failure Modes We Fix in Production
A client called us last month with a simple complaint: "Our support agent confidently quotes the...
You Spent $35,000 Fine-Tuning a Model. A $28,000 RAG System Would Have Done It Better.
The most expensive mistake in enterprise AI right now is fine-tuning when retrieval is the actual...
Headroom: a Tool to compress everything your AI Agent reads — tool outputs, logs, RAG chunks, files, and conversation hi...
Headroom: a Tool to compress everything your AI Agent reads — tool outputs, logs, RAG chunks, files, and conversation history — before it reaches the LLM - 60-95% fewer Tokens, sam...