Maswali yanayounganishwa na mwongozo · Kati Kiwango

Maswali ya Usimamizi wa Bidhaa za AI

Hujaribu matatizo ya mtumiaji, njia mbadala rahisi, vipimo vya bidhaa, kutokuwa na uhakika, tathmini, kushindwa, kurejesha uwezo wa kufikia matokeo na kutoa mafunzo.

Njia za mwongozo zinazohusianaUsimamizi wa Bidhaa za AI
Swali 1 ya 8

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

Hakiki kila swali8 maswali

Majibu na maelezo huonekana unapojibu maswali.

  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
    • The model’s parameter count
  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
    • A longer product name
  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