How do you implement a self-correcting RAG loop where t...

The core design decision in a self-correcting loop is to diagnose before retrying. Generating an answer and just re-running the same pipeline again if it scores poorly wastes a retry on a strategy that already failed. Instead, the evaluation step should distinguish between failure types: low faithfulness means the model is generating content not actually supported by the context, which calls for a stricter grounding instruction; low relevancy means the retrieved chunks were the wrong ones, which calls for rewriting the query or switching search strategy; and an answer that’s accurate but incomplete calls for breaking the question into sub-questions and answering each.

This only works safely with a hard ceiling on retries — three attempts is a reasonable default — because without one, a query the system genuinely can’t answer well will loop indefinitely. When the cap is hit, the system should return its best attempt with an explicit caveat about limited confidence rather than silently returning a low-quality answer as if it were fully reliable.