Eval Harness Skill

Eval-driven development defines expected AI behavior through unit tests before coding begins. It uses capability evaluations to test new functionalities and regression evaluations to ensure existing features remain unaffected during implementation.

AI reliability metrics track consistency using pass@k and pass^k measurements. These metrics integrate code-based, model-based, and human grading results to quantify performance during capability and regression evaluations.

Implement a formal evaluation framework by defining capability and regression evaluations, integrating code-based, model-based, and human grading, and tracking pass@k and pass^k metrics to ensure structured AI feature maintenance.

Yes, AI testing supports combining code-based, model-based, and human grading methods. Integrating these varied grading approaches within capability and regression evaluations provides comprehensive reliability tracking using pass@k and pass^k metrics.

Run regression evaluations before adding new AI features to prevent unintended side effects. They ensure existing functionality remains intact, maintaining overall system reliability alongside capability evaluations during feature implementation.