eval-harness

Eval-driven development is a methodology where evaluations define expected behavior before implementation. This framework provides a structured way to evaluate AI code generation, ensuring reliability and tracking progress through formal evaluation methods.

You can measure AI code generation reliability using pass@k and pass^k metrics provided by this framework. These metrics track the consistency and success rate of AI-generated code across multiple evaluation runs.

You can set up regression testing by defining capability and regression evaluations within the framework. This ensures existing functionality remains intact when developing new AI features by running automated evaluations against your code.

Yes, the framework supports grading outputs using code-based, model-based, or human graders. This allows you to choose the evaluation method that best fits your development workflow and capability requirements.

You can integrate continuous evaluation by defining success criteria for new AI features using this framework, then running automated evaluations to confirm requirements are met and existing code is not broken during development.

The framework requires defining explicit success criteria and evaluations before implementation, which may add overhead to the development process. It is designed for formal benchmarking and continuous evaluation rather than rapid, unstructured prototyping.