Retrieval-Augmented Generation (RAG) is an approach in artificial intelligence that improves the way language models answer questions by connecting them to external knowledge sources.

Instead of relying only on what the model learned during training, RAG systems first retrieve relevant information from a database, documents, or knowledge base, and then use that information to generate a response.