eval-driven-dev

To use evals as unit tests for AI workflows, you define success criteria before implementation, then run capability and regression evals to measure outcomes and iterate fixes. This formalizes quality assurance for AI pipelines and prompt engineering.

Eval-driven development for prompt engineering is a validation method that applies capability and regression evals to measure AI workflow quality. It ensures measurable outcomes by defining success criteria before implementation and scoring results with pass@k and pass^k metrics.

To prevent reviewer bias when evaluating AI agent pipelines, separate the evaluator context from the implementer context. This key separation ensures objective grading when using model-based, code-based, or human-based graders to validate AI workflows.

To validate AI workflow regressions, use pass@k and pass^k metrics. These metrics define success rates across multiple trials, allowing you to measure outcomes accurately and guide release decisions for new AI features and agent pipelines.

You need capability evals to validate new AI features and regression evals to ensure no regressions occur when modifying existing AI workflows. Both eval types use code-based, model-based, or human-based graders to measure outcomes and iterate effectively.

Yes, you need clearly defined success criteria before building an AI agent pipeline. Defining evals before implementation allows you to measure outcomes accurately after coding and iterate fixes based on pass@k and pass^k metrics.