A technique that supplements a language model's generation with documents retrieved at runtime from an external store, rather than relying solely on knowledge baked into model weights. The retrieved text is injected into the context window before the model produces its answer, letting the system access up-to-date or domain-specific information without retraining. RAG trades retrieval quality for generation quality — garbage in, garbage out still applies.