概述
It includes deciding whether AI is appropriate, defining acceptable failures, and planning evaluation and operation. A high model score does not automatically mean that a feature helps its users.
主要要点
- Begin with the user problem.
- Separate model and product measurements.
- Plan failure handling and ongoing evaluation.
深入探讨
Start with the task and the current alternative. Identify what users are trying to complete, where they struggle, and what a successful outcome looks like. Compare a model-based approach with simpler software or a clearer process before committing to added complexity. Separate model metrics from product metrics. Prediction accuracy, retrieval recall, or output preference can help diagnose a system. Task completion, user effort, error recovery, and the cost of a useful outcome address whether the product actually improves the workflow. Define the boundaries of acceptable behavior. Include unsupported requests, uncertainty, latency, and the actions requiring review. Plan how users can correct mistakes, cancel work, or reach another route when the model cannot help. Release with a clear evaluation and monitoring plan. Record model and prompt versions, measure outcomes on representative users and tasks, and investigate regressions. Avoid turning a demonstration into a general promise before the product has evidence under real operating conditions.
04工作示例
Choose a useful success metric
Imagine a support assistant that closes more tickets after a change, but customers reopen many of them.
Measure resolved issues and repeat contact alongside closure rate.
Investigate whether the change improved answers or merely made it easier to mark unresolved work complete.
它显示了什么
The constructed example separates an operational count from the user outcome it is meant to represent.
现实世界的实施
Define success as completing a user task with acceptable effort and error rates.
Compare an AI feature with the existing workflow using the same outcome criteria.
付诸实践
为什么这很重要
- 构建选择. 应用级设计决定了人工智能是否能改善实际结果。
- 团队与工作流程. 良好的工作流程集成可以创造用户值得信赖的生产力收益。
- 风险与安全. 范围明确的用例可以减少变更疲劳和实施风险。
留意
- 将损坏的流程自动化可能会加剧现有问题。
- 团队可能会过度自动化并消除所需的人工判断。
- 如果不持续评估输出，质量可能会出现偏差。
后续步骤
- 绘制当前工作流程并确定摩擦最大的步骤。
- 在完全自动化之前定义人工检查点。
- 对用户进行提示、升级路径和质量标准方面的培训。
- 跟踪任务级结果以确认持续价值。
资料来源与延伸阅读
- GoogleFraming an ML problem
不断探索
常见问题
Should a product team choose the model before defining the feature?
Start with the task, constraints, and success criteria. Those requirements should guide whether and how a model is used.