model
Human-calibrated eval scoring
Automated eval scores need human calibration so the measured result still matches the product question.
正文
A numeric score can hide whether an eval is measuring the right thing. Human-calibrated scoring pairs automation with human review: inspect examples, compare scores with expert judgment, and update the rubric when the metric rewards the wrong behavior.
This is especially important for AI product ideas where usefulness, specificity, safety, and user fit are partly qualitative. A score gate should therefore expose examples, not only a number.
来源引用
Evaluation best practices
Source: Evaluation best practices
OpenAI recommends combining metrics with human judgment and maintaining agreement between human feedback and automated scoring.
相关卡片
Scoring gate
A scoring gate gives a generated idea a lightweight decision point before it receives more time.
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.
Source-first card review
A Knowledge Atlas card should be reviewed against its source reference before it becomes a trusted browsing or search result.
所在阅读路径
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.