Retrieval Augmented Generation (RAG) enhances LLM outputs with retrieved external information for accurate, current responses. Core pattern: User query → retrieve relevant documents from knowledge base → inject documents into LLM context → generate grounded response. Why RAG?: LLMs have knowledge cutoffs, may hallucinate, can't access private data. RAG provides current, verified, domain-specific information. Components: Document store (chunked, embedded), vector database, retriever, reranker (optional), generator (LLM). Pipeline variations: Naive RAG, advanced RAG (query rewriting, reranking), modular RAG (customizable stages). Quality factors: Retrieval precision/recall, chunk quality, context window usage, generation faithfulness. Applications: Enterprise search, customer support, research assistants, code assistants, domain experts. Frameworks: LangChain, LlamaIndex, Haystack, custom implementations. Challenges: Retrieval failures, context length limits, maintaining freshness, evaluation. Optimization areas: Embedding models, chunking strategy, retrieval algorithms, reranking, prompt engineering. RAG is the dominant pattern for production LLM applications requiring accuracy.