安全评估 / NAVIN RESEARCH
Continuous AI Evaluations in CI and Production
Run fast regression gates on every change and monitor sampled real-world outcomes without leaking user data.
Run fast regression gates on every change and monitor sampled real-world outcomes without leaking user data.
简要回答: Use a layered eval program: small deterministic checks in each pull request, broader offline suites before release, canary comparisons during rollout, and privacy-reviewed production monitoring. Every failure should map to an owner and a release decision.
实施步骤
- Version datasets, graders, prompts, tools, and model configuration.
- Set blocking thresholds for critical task and safety slices.
- Keep a private holdout set to detect overfitting.
- Sample and redact production cases under a documented policy.
验证清单
- Track confidence intervals and repeated-run variance.
- Alert on slice regressions even when the average improves.
- Verify rollback is fast and tested.
常见错误
- One giant eval run nobody can interpret.
- Updating tests to make a failing release pass.
- Storing sensitive production traces indefinitely.
实际决策
先交付能够通过上述检查的最小设计。记录测试中使用的模型、提示词、工具、数据集与运行时版本，让后续变化可以被比较而不是被猜测。依赖供应商的限制、模型名称、价格或预览功能之前，请重新检查链接文档；这些细节变化往往快于本文的工程原则。
