eval-harness

Regression testing for AI models is executed by treating evaluations as unit tests to track regressions, ensuring existing functionalities are not broken by new changes. The framework integrates into development workflows for continuous testing and reliability tracking.

Eval-driven development (EDD) is a principle that promotes defining expected behavior before implementation and continuous evaluation. It evaluates AI capabilities by creating specific tests for new functionalities to ensure models meet defined criteria.

To set up a capability evaluation for a new AI feature, use the eval-harness skill to define specific tests for its capabilities, such as verifying if it can generate code for a specific task. This ensures quality and reliability throughout the development cycle.

The framework supports code-based, model-based, and human grading mechanisms for comprehensive AI assessment. These integrated grading mechanisms allow teams to formalize evaluations and ensure AI models meet defined criteria through multiple verification methods.

Reliability assessment in AI evaluation frameworks uses defined metrics like pass@k and pass^k. These metrics help quantify the reliability of AI models during capability and regression evaluations within the development workflow.