Retrieval-Augmented Generation — a technique that grounds an LLM's responses in retrieved external data rather than relying solely on its training.
RAG solves a core LLM limitation: models don't know about your private documents or recent events. By retrieving relevant content first and feeding it to the model, RAG produces more accurate, current answers.
