Self-RAG (Self-Reflective Retrieval-Augmented Generation) is an approach where an AI system evaluates and refines its own retrieval and generation process during response creation. It retrieves information, assesses the relevance and quality of that information, and iteratively improves the output by validating or correcting its reasoning. This self-evaluation loop helps improve accuracy, reduce hallucinations, and ensure that responses are grounded in reliable data.