#RAG
287 related articles
Tired of Manually Tuning RAG? Open-Source Tool Muffakir Automates Your Experiments
Muffakir is an open-source RAG experiment runner that automates chunking, embedding, retrieval, reranking, and top-k combinations, comparing results by metrics, latency, and cost.
Building a RAG Knowledge Base Q&A System from Scratch: A Full Walkthrough with DeepSeek Integration
Build a RAG private knowledge base Q&A system from scratch with Python and DeepSeek. Learn document upload, vector indexing, and retrieval-augmented generation.
Hot AI Debates: Is RAG Dead? Do Skills Replace MCP?
GitHub Podcast tackles three hot AI debates: should you read AI-generated code line by line, is RAG obsolete, and will Anthropic Skills replace MCP? A deep-dive analysis.
FAISS Vector Search in Practice: Lessons Learned From Embeddings to RAG
A developer shares hands-on lessons building FAISS vector search and RAG pipelines, covering embeddings, metadata filtering, query rewriting, and hybrid retrieval.
3-Month LLM Learning Roadmap: From Zero to Agent Development, Fully Broken Down
A 3-stage LLM learning roadmap for beginners: master prompts and APIs, build RAG-powered knowledge bases, then develop Agents with ReAct and multi-agent collaboration.
RAG Knowledge Base: A Complete Guide from Fundamentals to Optimization
A complete guide to building a RAG knowledge base: covering vector databases, chunking, embedding models, retrieval strategies, re-ranking, and agent workflows to minimize LLM hallucinations.
Industrial-Grade AI Agent Development Guide: From Demo to a Four-Layer Architecture That Can Handle Real Business
A complete guide to building industrial-grade AI Agents: covers the four-layer architecture, Planner, Tools, Memory, the ReAct loop, vector databases, RAG, and enterprise stability.
Layout-Aware PDF Parsing in Practice: Solving the RAG Chunking Problem
Learn how to build a layout-aware PDF parser with Python and pdfplumber to solve RAG chunking issues in multi-column documents using visual grid coordinate detection.
rag-eval: A Zero-Dependency, No-API-Key RAG Evaluation Tool
rag-eval is a zero-dependency, framework-agnostic open-source RAG pipeline evaluation tool. It supports free local lexical and retrieval metrics with no API keys required, and offers optional LLM Judge for semantic validation. Compatible with Haystack, LangChain, and LlamaIndex.
Explainable Agentic Retrieval: Making RAG More Than Just 'Guessing the Right Answer'
Pure vector RAG agents retrieve content but can't explain their reasoning. This article explores explainable agentic retrieval using Neo4j knowledge graphs, hybrid vector-graph search, and multi-step entity extraction for traceable, source-backed AI Agent answers.
AI Large Model Learning Roadmap for Beginners: Three Stages from Prompt Engineering to Agent Development
A complete AI learning roadmap for beginners: from prompt engineering and API calls, to RAG knowledge bases, to Agent and multi-agent development — a 3-stage framework to go from zero to building real AI applications.
Spring AI LLM Tutorial for Beginners: A Complete Learning Path from Core Principles to Enterprise Practice
A breakdown of a popular Spring AI LLM course on Bilibili — covering Transformer architecture, RAG, private deployment, fine-tuning, and enterprise projects for Java developers.
Turning SQLite into a Vector Database: A Hands-On Comparison of Two Extensions
Compare sqlite-vec and sqlite-vector extensions for turning SQLite into a vector database. Covers virtual tables, metadata filtering, quantization, and speed/memory/disk benchmarks for RAG use cases.
Hierarchical RAG Architecture Research: How Independent Developers Can Break Into Academic Research
An indie developer on Reddit seeks IR professor guidance for hierarchical RAG research. This article explores the technical background and practical advice for independent AI researchers facing academic barriers.
NepKANUN: A Deep Dive into Nepal's RAG-Based Legal AI Assistant
NepKANUN is a Nepali legal AI assistant built on RAG and fine-tuned LLMs, achieving BERTScore F1 up to 0.82 — a replicable blueprint for low-resource domain AI.
AI Agent Beginner's Guide: Core Architecture and How Intelligent Agents Work
A complete beginner's guide to AI Agents: covers the definition, large model evolution, perception/brain/action architecture, and memory mechanisms like RAG and System Prompt.
RAG Enterprise Knowledge Base in Practice: A Complete Guide from Principles to Optimization
A complete guide to building RAG enterprise knowledge bases: from retrieval-augmented generation principles to vector database selection, chunking, embedding, retrievers, and reranking optimization.
Spring AI in Practice: RAG, MCP, and Agents for Java Developers
A hands-on Spring AI course for Java developers covering LLM integration, prompt engineering, RAG, MCP, and Agents with three real-world projects.
The Cost Reduction Dilemma in AI Verification Systems: How to Read Less Evidence Without Missing What Matters
The real cost in AI verification pipelines isn't retrieval — it's how much evidence must be read. This post examines why early stopping, slice skipping, and deduplication fall short, and the core challenge of preserving minority evidence.
Perplexity Reveals: The Embedding and Ranking Model Serving Infrastructure Behind AI Search
Perplexity reveals the serving infrastructure behind its AI search embedding and ranking models, showing how retrieval quality shapes answer accuracy and speed.