Models do not need to memorize all knowledge in their parameters — retrieval-augmented generation lets them look up facts at inference time, reducing hallucination and enabling knowledge updates.
Combines a pre-trained retriever with a pre-trained generator, allowing the model to access external knowledge during generation. The retriever fetches relevant documents which condition the generator output.