RAG (retrieval-augmented generation)

RAG retrieves passages from a corpus (via vector similarity, keyword search or both) and injects them into the prompt so the model answers from current, specific data rather than training memory. In agents it is usually one tool among several ('search the docs') rather than a fixed pre-step, a shift sometimes called agentic RAG.