Eval design needs “model empathy,” not just harder tasks
i_dg23 · x · 2026-07-22
A short thread argues that good evals require “model empathy”: you need to understand how a model will perceive the task, not just how a human engineer would.
- The author says even a decent iOS engineer would rage-quit if handed this task.
- The point is that evals should be designed around the model’s failure modes and incentives.
- In practice, this means making benchmark tasks realistic enough to reveal where an agent actually breaks.
Related event: Researchers Propose New Framework for AI Evaluation Design(3 posts)→
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