An AI architecture that combines language model generation with real-time retrieval from external knowledge sources. This approach improves accuracy by combining language generation with real time access to external data. It is widely used in enterprise and search based AI systems. For architectural analysis, see our systems article on RAG implications.
RAG addresses key limitations of pure language models by grounding responses in retrievable, verifiable sources.
RAG systems retrieve relevant documents before generation, providing context that helps produce more accurate, up-to-date, and verifiable responses.
Retrieval-augmented generation allows AI systems to cite current information not present in their training data.