RAG

RAG is an architecture that enhances language model responses by retrieving relevant documents from an external knowledge base and including them in the prompt context at inference time. It combines the parametric knowledge of LLMs with the up-to-date, domain-specific knowledge stored in vector databases or search indexes.

RAG reduces hallucinations, improves factuality, and enables knowledge base updates without retraining.