What is Self-RAG and how does it differ from standard R...

Standard RAG always retrieves regardless of whether the query needs it, and Agentic RAG relies on an orchestration layer sitting outside the model to decide when to retrieve. Self-RAG moves that decision inside the model’s own generation process: the model is trained to emit special tokens signaling whether it needs to retrieve at all, whether a retrieved chunk is actually relevant, whether its draft answer is supported by that chunk, and whether the final response is useful — all without a separate decision-making component.

The practical benefit is that Self-RAG skips retrieval entirely for questions the model already knows the answer to, which saves both latency and cost on a meaningful share of everyday queries. The catch is that this behavior has to be trained into the model through fine-tuning, so it’s really only viable with open-weight models you control — you can’t get this behavior out of a closed API model just by prompting it.