Model evals miss the point when they ignore tail reliability

eugeneyan · x · 2026-07-23

The post argues that model evals often overfocus on the median task, while real projects are won or lost in the tail.

Teams tend to optimize for the average/median case, but completion risk lives in the hard edge cases.
In production work, reliability matters more than raw benchmark capability.
The author says models should be treated as collaborators rather than mere tools: explain intent, let them research, push back, define specs, execute, and verify over hours or days.
The accompanying chart contrasts the median task with the tail, where careful models are the difference between success and failure.

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