एआई मूल्यांकन मूल बातें प्रश्नोत्तरी

जैसे ही आप प्रश्नोत्तरी लेते हैं उत्तर और स्पष्टीकरण दिखाई देते हैं।

1. एआई प्रणाली का मूल्यांकन करने से पहले एक टीम को क्या परिभाषित करना चाहिए?
   • 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. विकास के उदाहरण एक आयोजित मूल्यांकन सेट से किस प्रकार भिन्न होने चाहिए?
   • 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. क्या होता है जब कोई टीम अपने अंतिम परीक्षण सेट पर बार-बार ट्यून करती है?
   • The test set becomes larger
   • The score becomes a baseline
   • The test set becomes another development set
   • The model automatically generalizes better
4. कौन सी तुलना दिखा सकती है कि क्या अतिरिक्त मॉडल जटिलता व्यावहारिक लाभ प्रदान करती है?
   • 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. सारांश के लिए, मार्गदर्शिका किस मूल्यांकन जाँच की अनुशंसा करती है?
   • Server uptime and account age
   • Pixel similarity and frame rate
   • Factual consistency and coverage
   • प्रशिक्षण युगों की संख्या
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