eval-harness

You can evaluate AI-generated code by implementing an Evaluation-Driven Development framework that defines success criteria upfront and runs automated code checks alongside model-based assessments to track regressions.

You can track AI code quality and reliability over time using pass@k and pass^k metrics, which gauge whether your generated code consistently meets predefined evaluation criteria across multiple runs.

Set up automated testing for AI code by defining model-based evaluators that use custom prompts to assess generated code, combining these AI-driven assessments with standard code checks like grep and npm test.

Yes, you can use npm test, grep, and build commands as code-based evaluators to automatically verify that your AI-generated features meet expected behavior and pass capability tests.

Evaluation-Driven Development is a framework that defines evaluation criteria before implementation to ensure AI-generated code reliability, needed when developing new AI features to prevent regressions and verify requirements.

No, the evaluation framework operates independently without external dependencies, allowing you to integrate evaluation definitions and reports directly within your existing project structure.