eval-harness

Eval-driven development defines evaluations before coding and runs continuous tests. This framework implements it to ensure reliable AI performance and catch capability issues early in the development cycle.

Initialize the eval harness and define your specific test cases to set up capability and regression testing. The framework supports various grading mechanisms to evaluate AI code quality and track reliability metrics.

Yes, the framework supports model-based graders for AI capability testing. It also offers code-based and human graders to thoroughly evaluate models and measure reliability using pass@k and pass^k metrics.

The evaluation framework tracks pass@k and pass^k metrics for measuring AI reliability. These metrics help development teams quantify performance, catch regressions early, and ensure consistent code quality.

No external dependencies are required to use this AI testing framework. It operates independently with scripts to implement the structured evaluation workflow for your AI development projects.

Run regression evals continuously throughout your AI development workflow to catch issues early. The framework integrates this process to track reliability metrics and ensure new code changes do not degrade model performance.