Retrieval-Augmented Generation
Also called: RAG
Fetching relevant documents first and putting them in the prompt, so the model answers from sources instead of memory.
RAG splits the job in two: a retrieval step finds passages relevant to the question, then the model answers using those passages as context. It is the standard fix for knowledge cutoffs, private data, and hallucination, because the model is grounded in text it can cite. Retrieval quality sets the ceiling — the best model cannot rescue the wrong three paragraphs.
In practice: A support bot that quotes your actual refund policy rather than a plausible-sounding invention.