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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