RAG (Retrieval-Augmented Generation)

RAG stands for retrieval-augmented generation, a name introduced in a 2020 paper by Patrick Lewis and colleagues (arXiv, May 2020; presented at NeurIPS 2020). It is the default pattern for getting an LLM to answer questions about content it was not trained on — your company wiki, a legal corpus, last week's Slack threads, internal product docs. How it works in one sentence: index your documents into a vector database, embed the user's question, pull the top-K nearest chunks, paste those chunks into the prompt as context, and let the model generate an answer that uses them. RAG vs fine-tuning. Fine-tuning bakes information into model weights — when the data changes, you re-train. RAG keeps the data in a separate retrievable layer, so when a policy changes, you re-index. RAG also gives you citations (you can show which chunk fed the answer) and works on day-zero data the model has never seen. Use fine-tuning for behavior and style; use RAG for knowledge. RAG vs long context. A 1-million-token context window means you can paste a lot in directly. RAG still wins when the document set is larger than the context, when you need citations, when latency matters, or when most of the corpus would be noise on any given query. Long context wins for short corpora where every word might matter. RAG vs plain search. Search returns documents; RAG returns answers. The model reads the retrieved chunks and writes the response in the form the user asked — a summary, a comparison, a recommendation — instead of just linking to sources. Common failure modes. Chunks lose context (a paragraph in the middle of a contract has no idea it belongs to Section 4). Embedding-only retrieval misses exact-match queries (product SKUs, error codes, legal citations). The retriever pulls 20 plausible-but-wrong chunks and the model confidently confabulates from them. Production RAG layers in hybrid search, reranking, query rewriting, and structured metadata filters to address these. Anthropic's Contextual Retrieval tests (September 2024) put a number on it: adding chunk-level context to both embeddings and BM25 cut top-20 retrieval failures by 49%, and by 67% once reranking was added. A modern RAG stack (2026). Chunk documents into ~500-token pieces with overlap → embed with Voyage (voyage-4, now part of MongoDB) or OpenAI text-embedding-3 → store in Pinecone, Qdrant, or pgvector → retrieve top-20 with hybrid search (BM25 + vectors) → rerank to top-5 with a cross-encoder → send to a current Claude, GPT, or Gemini model with a system prompt that says "answer only from these documents." For the full teaching version — how each step actually works, when to use RAG, when not to, and the failure patterns to watch for in beginner systems — read [How RAG works, and when to use it](/learn/ai/build-with-ai/how-rag-works-and-when-to-use-it).