eval-harness

You can run regression testing for Claude Code tasks by applying eval-driven development principles through a structured evaluation framework, executing capability and regression eval templates, and using metrics like pass@k and pass^k to track reliability over iterations.

Eval-driven development for AI coding projects is a structured approach to defining, running, and interpreting evaluations to ensure AI code reliability. It automates validation of AI-assisted coding tasks and prompt design revisions to track regressions across model updates.

Yes, you can use multiple graders for AI code evaluation. The framework supports multi-grader workflows including code-based, model-based, and human verification graders to provide robust validation for Claude Code sessions.

You measure AI code generation reliability with pass@k and pass^k metrics by running structured evals that quantify task success rates across multiple iterations. These metrics validate capability and regression performance to track reliability over time.

Yes, the evaluation framework works without external dependencies. It operates independently to automate structured evaluation and validation of Claude Code tasks, supporting capability and regression evals with metrics-driven validation out of the box.

The best way to validate prompt design revisions in AI-assisted coding is using a formal eval framework that automates structured evaluation. It applies eval-driven development principles to run capability and regression evals with multi-grader support for robust verification.