Maswali yanayounganishwa na mwongozo · Rahisi Kiwango

Maswali ya Msingi ya Tathmini ya AI

Weka vigezo vya kukubalika, tenga maendeleo kutoka kwa data iliyozuiliwa, linganisha na msingi, chagua ukaguzi unaolingana na kazi, na uweke hati ya kutokuwa na uhakika na mipaka.

Njia za mwongozo zinazohusianaMisingi ya Tathmini ya AI
Swali 1 ya 8

What should a team define before evaluating an AI system?

Hakiki kila swali8 maswali

Majibu na maelezo huonekana unapojibu maswali.

  1. What should a team define before evaluating an AI system?
    • Acceptance criteria for inputs, expected outputs, tolerable errors, and escalation
    • A press release about model quality
    • A single average score
    • A deployment date only
  2. How should development examples differ from a held-out evaluation set?
    • Use the held-out set for prompt tuning every day
    • Use development examples for iteration and reserve held-out examples to test choices
    • Mix both sets before every run
    • Keep no record of which examples were used
  3. What happens when a team repeatedly tunes on its final test set?
    • The test set becomes larger
    • The score becomes a baseline
    • The test set becomes another development set
    • The model automatically generalizes better
  4. Which comparison can show whether added model complexity provides practical benefit?
    • Compare only with last week’s best score
    • Compare with a simple baseline
    • Compare against a random deployment date
    • Compare with a different task’s benchmark
  5. For a summarizer, which evaluation checks does the guide recommend?
    • Server uptime and account age
    • Pixel similarity and frame rate
    • Factual consistency and coverage
    • Idadi ya vipindi vya mafunzo
  6. How should a team verify an AI agent’s completed action?
    • Trust its success message
    • Count how many tools it called
    • Check the authoritative final state and unintended side effects
    • Ask the same model whether it succeeded
  7. Why might a rare failure deserve more attention than many harmless wording differences?
    • Consequences of the failure can outweigh frequent low-impact variations
    • Rare events are always measurement errors
    • Wording differences cannot be evaluated
    • The average score must be ignored
  8. What should an evaluation document about its limitations?
    • That every real-world case was tested
    • Where the evaluation does not represent actual use
    • Only the name of the model
    • That uncertainty has been eliminated