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Evaluation best practices
OpenAI guidance on designing task-specific evaluations, combining metrics with human judgment, using logs, and treating evaluation as a continuous process.
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Eval-driven AI development
An AI product should define how success will be evaluated before the team invests in deeper prompt, model, or workflow work.
OpenAI recommends evaluating early and often, with scoped tests at each stage.
Human-calibrated eval scoring
Automated eval scores need human calibration so the measured result still matches the product question.
OpenAI recommends combining metrics with human judgment and maintaining agreement between human feedback and automated scoring.
Production log to eval case
Useful production logs can be converted into eval cases so real failures become repeatable tests.
OpenAI recommends logging during development so logs can later be mined for useful eval cases.
Task-specific eval objective
A useful AI eval starts with a task-specific objective that names what the system must do well in its real product context.
OpenAI distinguishes task-specific application evals from generic benchmarks and starts the eval workflow with an objective.
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AI idea validation to eval
A source-backed path for turning generated AI product ideas into problem framing, validation briefs, task-specific evals, and score-gated decisions.