eval-harness

To evaluate AI coding reliability in Claude Code sessions, define eval plans with explicit success criteria, run regression tests against stored baselines, and quantify results using pass@k metrics.

Eval-driven development for AI coding assistants is a framework that rigorously tests capability and regression scenarios, applying formal graders to ensure reliable, testable outcomes across code updates.

Structure AI evaluation workflows for regression testing by defining eval criteria, implementing tests, running evaluations against stored baselines, and generating reports to track progress over time.

Yes, you can use model-based graders for AI evaluation alongside code-based and human graders to balance deterministic testing with nuanced evaluation of complex coding outcomes.

Pass@k and related metrics quantify AI reliability progress during eval-driven development by measuring how consistently an AI coding assistant meets defined success criteria across multiple attempts.

Store eval definitions, baselines, and run histories using structured storage best practices to ensure repeatable assessments and accurate tracking of regression and capability evals over iterations.