What is self-RAG and how does it improve answer reliability?

Self-RAG is a framework where the LLM is trained to adaptively retrieve, critique, and generate responses with self-reflection. The model learns special reflection tokens that indicate whether retrieval is needed for the current query, whether retrieved passages are relevant, whether the generated response is supported by the retrieved passages, and whether the overall response is useful. This creates a self-aware RAG system that retrieves only when necessary, evaluates the quality of its own retrieval, critiques its own generations for faithfulness, and revises responses that fail self-checks. Self-RAG achieves better factual accuracy than standard RAG because the model actively verifies its outputs rather than blindly generating from provided context. The training requires a specialized dataset with reflection annotations, and the resulting model can be fine-tuned from open-source base models. This approach significantly reduces hallucination and improves the reliability of RAG system outputs.