Rag

Introduction Retrieval-Augmented Generation (RAG) pairs a retriever (to fetch relevant context) with a generator (an LLM) to produce accurate, grounded answers. This pattern reduces hallucinations, lowers inference costs by offloading knowledge into a searchable store, and makes updating knowledge as simple as adding or editing documents. In this guide, we’ll move from beginner-friendly RAG to advanced techniques, with practical code examples along the way. We’ll cover chunking, embeddings, vector stores, hybrid retrieval, reranking, query rewriting, multi-hop reasoning, GraphRAG, production considerations, and evaluation. A final resources chapter includes links to papers, libraries, and tools. ...