Self-RAG is an approach where an AI system evaluates and refines its own retrieval and generation process during response creation. The system retrieves information, assesses the relevance and quality of what it retrieved, and iteratively improves the output by validating or correcting its reasoning before producing a final answer.
This self-evaluation loop catches situations where the initial retrieval missed something important or where the generated answer does not actually follow from the retrieved content. By checking its own work before committing to a response, Self-RAG produces more accurate and better-grounded outputs on tasks where a single retrieval step might miss important nuance.
