Retrieval-augmented generation (RAG) combines traditional language model generation with the ability to retrieve relevant information from an external knowledge base. This approach enhances the model’s ability to answer questions accurately by grounding its responses in real data, making it crucial for tasks requiring up-to-date information or specific knowledge.