Retrieval-Augmented Generation (RAG) retrieves relevant external information at query time and supplies it to a language model before generation. It is useful when answers depend on private, changing, large, or domain-specific knowledge that may not be reliably present in model weights.

RAG is not a training method: the usual pattern leaves model weights unchanged and changes the context for a request. Quality depends on source quality, parsing, chunking, indexing, retrieval, ranking, context assembly, generation, citation validation, and evaluation. Retrieval can miss the right passage; sources can conflict; a model can misread good evidence. RAG improves grounding opportunities but does not guarantee truth.