eval-harness

Eval-driven development defines evaluations before coding to ensure AI-assisted development reliability. It measures performance using pass@k and pass^k metrics to quantify AI-generated code quality through formal capability and regression testing.

To set up regression testing for AI-generated code, define capability evaluations specifying expected behavior and success criteria upfront. Execute these evals to verify the AI's implementation and track its performance over time.

Yes, AI evaluation supports code-based, model-based, and human grading mechanisms. This comprehensive assessment approach allows you to combine automated metrics tracking with manual review for evaluating AI-generated outputs.

The best way to track AI code quality metrics is by implementing a formal evaluation framework. It facilitates defining, executing, and reporting capability and regression evaluations to continuously measure AI reliability using pass@k and pass^k metrics.

Yes, formal AI evaluation works without external dependencies. The framework implements eval-driven development principles natively, allowing you to define and run capability evaluations independently using code-based, model-based, and human grading mechanisms.