AI evaluation shouldn't just look at single-turn answers
Zhou_Yu_AI · x · 2026-07-14
The author points out that "getting every answer right" does not equal "getting the task done". Using access permission approval as an example: a model might act professionally, relevantly, and accurately at every step, but if it grants permissions to the wrong person, it ultimately fails the user.
They argue the issue lies in the granularity of current evaluations: we typically score individual responses instead of covering the entire task chain. A more rational evaluation should focus on:
- Whether the agent asks the right clarifying questions
- Whether it can maintain context in long conversations
- Whether it can recover from errors
- Whether the user actually gets their desired result
The conclusion is that AI testing shouldn't just center on model outputs, but return to whether the user's task is actually accomplished.
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