[thisischayan]

The approach that has worked for us in production is correction during generation, not after.

The model verifies its output against the rules in the prompt as it generates and corrects itself within the same API call — no retries, no external validator. If there are still failures the model cannot fix at runtime, those are explicitly flagged instead of silently producing wrong output.

This does not mean hallucinations are completely solved. It turns them into a measurable engineering problem. You know your error rate, you know which outputs failed, and you can drive that rate down over time with better rules. The system can also self-learn and self-improve over time to deliver better accuracy.