Retrieval-augmented generation (RAG)

Answering questions by first retrieving relevant passages from your documents and then giving them to the model as context. RAG lets a model use private or up-to-date knowledge without retraining, and makes answers checkable against sources. Its quality depends heavily on how documents are split, searched and ranked.