eval-harness

Eval-driven development treats evaluations as unit tests for AI development, defining capability and regression tests before implementation. This framework formalizes AI development by continuously evaluating model capabilities and tracking performance metrics like pass@k to ensure reliability.

You can track AI model regressions by defining and running capability and regression evaluations throughout the development workflow. This framework supports pre-implementation definition, in-progress checks, and post-implementation reporting to ensure existing functionality remains intact.

For AI model evaluation, you can use code-based, model-based, and human grading mechanisms. These comprehensive grading options enable rigorous testing of new AI features to ensure they meet performance benchmarks without breaking existing functionalities.

Yes, you can integrate continuous evaluation into your AI development workflow. This framework supports pre-implementation definition, in-progress checks, and post-implementation reporting, enabling development teams to rigorously test features before deployment.

To evaluate AI capabilities before deployment, define a new capability evaluation for the feature using this framework. It enables continuous evaluation of model capabilities and regressions, ensuring the feature meets performance benchmarks and doesn't break existing functionalities.

Yes, this evaluation framework supports tracking performance metrics like pass@k for AI testing. It formalizes AI development through eval-driven principles, ensuring reliability and tracking regressions through formal evaluation processes.