模型 / NAVIN RESEARCH
An AI Model Upgrade Playbook With Safe Rollback
Change models through pinned versions, regression evals, shadow traffic, canaries, and reversible configuration.
Change models through pinned versions, regression evals, shadow traffic, canaries, and reversible configuration.
简要回答: Treat a model upgrade like a dependency and behavior change. Pin the candidate where possible, run offline regressions, shadow representative traffic, canary a small share, and preserve a one-step rollback. Prompts and tool schemas may need migration even when the API shape stays stable.
实施步骤
- Capture the current model, prompt, tool, and retrieval baseline.
- Run fixed regression and fresh holdout evaluations.
- Compare cost, latency, refusals, formatting, and tool behavior.
- Canary with automatic rollback thresholds.
验证清单
- Inspect regressions by task slice.
- Test fallback compatibility before rollout.
- Store configuration revisions with each trace.
常见错误
- Switching a floating alias globally.
- Changing model and prompt in one untraceable release.
- Evaluating only average quality.
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。
