RAG separates what the model knows from what it can look up. The retrieval step is where your content enters the answer, which is why being in the index and easy to extract matters more than waiting for a model to be retrained on you.
The name describes the sequence: retrieve relevant documents, augment the prompt with them, then generate the answer. The technique was introduced in a 2020 paper by researchers at Facebook AI. Engines retrieve a candidate pool, evaluate it for authority and agreement, then generate an answer citing the strongest sources.