#RAG
287 related articles
Hierarchy-Aware RAG Chunking Toolkit: A Private Deployment Solution Built for Legal Documents
A hierarchy-aware RAG chunking toolkit with legal cross-reference extraction scripts, supporting private on-premise deployment and full source code delivery. Optimized for structured documents like legal texts, compatible with LangChain.
Enterprise RAG with LLMs: Building a Q&A System Architecture from Scratch
Week 1 of an enterprise RAG course: build a Q&A system with FastAPI, unify local and online LLM calls, deploy on Linux via PyCharm remote, and validate with Postman.
Building an Enterprise RAG Knowledge Base from Scratch: A Complete Practical Guide
A hands-on guide to building an enterprise RAG knowledge base with Vue 3, Spring AI, and Milvus — covering chunking strategy, recall optimization, hallucination suppression, source attribution, and access control.
Multi-Agent Systems in 3 Minutes: From Stateless Models to Collaborative Architecture
Understand multi-agent AI systems in 3 minutes: from LLMs as stateless functions to agent loops, memory, RAG, orchestrator architecture, and the MCP protocol.
Chunkr: A Rust-Powered Text Chunking Library That's 20x Faster
Chunkr is an open-source Rust text chunking library supporting recursive, Markdown, and hierarchical strategies plus native PDF loading — benchmarked at 15–20x faster than LangChain and pypdf for RAG preprocessing.
LangChain + Amazon Bedrock: How Agentic Retrieval Tackles Multi-Part Questions
Learn how agentic retrieval with LangChain and Amazon Bedrock Managed Knowledge Bases handles complex multi-part queries that single-shot RAG can't, with trace event comparisons and cost analysis.
Naive RAG vs. Advanced Hybrid RAG: Two Paradigms of Retrieval-Augmented Generation
A clear comparison of Naive RAG vs. Advanced Hybrid RAG: from single-pass vector retrieval to hybrid architectures combining vector search, BM25, metadata filtering, and cross-encoder re-ranking.
Hybrid RAG Retrieval in Practice: A Complete Guide to Dense+BM25+RRF and Reranking
A deep dive into ReRankEval's hybrid RAG pipeline: how dense vector search, BM25, RRF fusion, and LLM reranking work together, evaluated with Hit Rate, MRR, and NDCG.
A New Paradigm for Contextual Embeddings: Perplexity Releases pplx-embed-v2 to Set New Benchmarks
Perplexity releases pplx-embed-v2-context-9b, a contextual embedding model that encodes chunks with full-document visibility, achieving SOTA on ConTEB and turbopuffer's context-bench benchmarks.
Production-Grade Agentic RAG in Practice: An Open-Source Course from Retrieval to Intelligent Agents
The open-source project jamwithai/production-agentic-rag-course focuses on production-grade Agentic RAG, with nearly 10K Stars. Learn about its core concepts and who it's for.
RAG Advanced Learning Guide: A Resource Roadmap from Prototype to Deep Mastery
A Reddit developer built a working RAG prototype but struggled to find deep, practical learning resources. This guide breaks down hybrid retrieval, document parsing, and evaluation — charting a path from 'it works' to 'I understand why.'
RAG Full-Stack Development: From Core Concepts to the Five Key Stages Explained
A beginner's guide to RAG: covers chunking, indexing, retrieval, reranking, and generation — plus vectors, Embedding, and vector databases. Build enterprise knowledge bases with confidence.
Enterprise Knowledge Retrieval Benchmarking: Why AI Agents Fail on Real-World Data
Why do AI agents fail on real enterprise knowledge bases? This article examines the gap between messy corporate data and clean benchmark datasets, and offers practical guidance for enterprise RAG evaluation.
Getting Started with AI Agent Development: A Practical Breakdown of the Four Core Modules
A beginner's guide to AI Agent development: break down the four core modules — prompt architecture, tool calling, RAG, and ReAct workflows — to fix hallucinations, loops, and unreliable output.
Building Production-Grade AI Systems from Scratch: A Complete Guide to LLMs, RAG, and Multi-Agent Workflows
A complete breakdown of production-grade AI systems: from LLM context windows and embeddings to RAG, LangChain, LangGraph, and MCP — built around one enterprise doc assistant project.
AI Quality Inspection for 400K Tickets: An Intern's Architecture Decisions
An intern solo-building an AI QA system for 400K support tickets. See how to split scoring into rule engine, embedding classifier, and RAG+LLM layers on a tight budget.
Agentic RAG Benchmark: Agent Loop Crushes Traditional Pipelines with 92.7% Accuracy
PipesHub tested 18 RAG pipelines on Google FRAMES: the best traditional pipeline hit 78.9%, while an Agent loop scored 92.7%. Key findings: rerankers can hurt accuracy, and models hallucinate cited answers.
DeepSeek + Ollama + AnythingLLM: Complete Guide to Local RAG Knowledge Base Deployment
Step-by-step guide to building a private local RAG knowledge base on Ubuntu using Ollama, AnythingLLM, and DeepSeek — covering Docker setup, model download, remote access config, and common pitfalls.
Staged RAG Architecture in Practice: Making Retrieval Failures Explicit and Controllable
A staged RAG architecture using BGE-M3 hybrid retrieval, BGE reranking, relevance gating, and context quality filtering to make retrieval failures explicit — built on Qdrant and local DeepSeek R1 7B.
AI Agent Development: From Zero to Enterprise-Grade Deployment
A practical breakdown of AI Agent development: framework selection, MCP tool calling, RAG data preparation, and production evaluation — what actually matters for enterprise deployment.