tool
Production log to eval case
Useful production logs can be converted into eval cases so real failures become repeatable tests.
正文
AI teams should not treat production mistakes as isolated anecdotes. When a user correction, support issue, or manual review exposes a failure, the case can be simplified, privacy-reviewed, and added to an eval set.
This turns operational learning into a regression guard. The next prompt or model change can be checked against cases that already mattered in the product.
来源引用
Evaluation best practices
Source: Evaluation best practices
OpenAI recommends logging during development so logs can later be mined for useful eval cases.
Production best practices
Source: Production best practices
OpenAI production guidance calls for security, privacy, compliance, testing, and proactive issue handling before broader release.
相关卡片
Eval-driven AI development
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Human-calibrated eval scoring
Automated eval scores need human calibration so the measured result still matches the product question.
Live continuous improvement
A live product still needs research, testing, accessibility checks, quality assurance, and performance metrics.
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.