Retrieval-Augmented Generation (RAG)
also: RAG
Fetching relevant documents and putting them in the prompt so the model answers from real sources.
RAG retrieves passages relevant to a query (usually by embedding similarity) and adds them to the context before generation, grounding answers in your data and reducing hallucination. Quality hinges on chunking, embedding, and how the top results are selected and ranked.