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问题 1 的 8

What should a team identify before selecting an AI model for a feature?

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  1. What should a team identify before selecting an AI model for a feature?
    • The user task, current alternative, constraints, and successful outcome
    • The vendor’s largest benchmark
    • A preferred architecture regardless of need
    • A public launch date
  2. Why can a high model score fail to show that a feature helps users?
    • High scores are always fabricated
    • Product metrics cannot be measured
    • Models cannot be evaluated before launch
    • Model metrics do not directly measure task completion or user effort
  3. A support assistant closes more tickets, but customers reopen them. What should the team examine?
    • Only the number of closed tickets
    • Whether the interface uses a chatbot
    • Resolution and repeat contact alongside closure count
    • 模型的参数数量
  4. What should the product define for unsupported requests and uncertain outputs?
    • A promise that the model will answer everything
    • Acceptable behavior, fallbacks, and ways to reach another route
    • A rule to conceal uncertainty
    • 更长的产品名称
  5. Why record model and prompt versions during release evaluation?
    • To prove every output is reproducible word for word
    • To avoid user research
    • To make future monitoring unnecessary
    • To investigate changes and regressions against the artifact actually used
  6. When should a team consider a simpler non-AI approach?
    • When it can solve the user problem with less complexity
    • Never; product value requires AI
    • Only after every model is tested
    • When a model score exceeds a threshold
  7. How should a team evaluate a new AI product change?
    • Use only a demonstration prompt
    • Use representative users and tasks, then measure product outcomes and failures
    • Count conversations regardless of completion
    • Compare different audiences without accounting for assignment
  8. Which user recovery feature fits a product with uncertain outputs?
    • Remove the ability to edit
    • Hide the model’s uncertainty
    • Let users correct mistakes, cancel work, or choose another route
    • Require users to repeat the entire task