An AI model must always explain why a task failed. The AI model must wait for user permission before trying again. Silent retries hide problems and waste time.
By Pawan Nayar · from the LLOS AI Learning record
An AI model must always explain why a task failed.
The AI model must wait for user permission before trying again. Silent retries hide problems and waste time.
When an AI model's task fails, the AI model must stop. The AI model must explain what went wrong. The AI model must say why the failure happened. The AI model must wait for the user to give permission to try again. Trying again without telling the user can hide problems. This can also waste a lot of time.
A developer asks an AI model to process a large dataset. The AI model tries three times over 45 minutes. Each time, the AI model fails silently without telling the developer. The developer does not know there is a problem. The developer thinks the task is still running. The developer waits for a result that will never come. The developer cannot fix the problem because the developer does not know about it.
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When a background task fails, the AI model must stop. The AI model must explain what broke and why.
When an API call fails, the AI model must state the exact error. The AI model must then find the problem in its own code.
Hardcoded fallback content can make the AI model seem unintelligent. It can show text that does not fit the specific question.
An AI model should never show internal codes like 'bandKey F/I/U/P' or 'section S1/S2'. It should not display format names like 'mcq_one_correct'.
An AI model must always explain why a task failed. The AI model must wait for user permission before trying again. Silent retries hide problems and waste time.