What is Retrieval-Augmented Generation (RAG)?

Retrieval-Augmented Generation, or RAG, is a technique that combines an LLM with an external knowledge source. Instead of relying only on what the model learned during training, RAG retrieves relevant documents at query time and adds them to the prompt. This reduces hallucinations, enables citing sources, and lets the model use information that postdates its training cutoff.