HITL Feedback Loop — Human Corrections to Improve AI

I will build a HITL feedback loop system — capturing every human correction to AI output, structuring corrections as training signal, and connecting the feedback pipeline to your model improvement or prompt optimisation process. HITL review that captures corrections without feeding them back into the AI system is human labour that improves individual outputs without improving the system — a feedback loop converts reviewer effort into systematic improvement, making each reviewer action more valuable than the immediate correction it produces.

The system covers correction capture at the review interface, correction structuring for training or prompt optimisation use, a corrections dataset management system, a prompt improvement pipeline using accumulated corrections, and a quality trend dashboard showing improvement over time.