AI Evals, Part 2: Error Analysis The Unglamorous Superpower Behind Good Evals

Dev.to / 6/13/2026

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Key Points

The post argues that most teams choose the wrong starting point for AI evals by jumping to generic metrics without first defining what failures to measure.
It frames “error analysis” as the highest-leverage step in the eval pipeline because it generates the real signal that downstream dashboards and processes operationalize.
It explains the “comprehension gap” between developers and how the model behaves on real inputs at production scale, noting that metrics cannot bridge this gap unless failure modes are already identified.
It describes error analysis as a deliberately low-tech loop: sample 50–100 real outputs, read them carefully, and open-code each failure with precise free-text notes about what went wrong.
The article positions error analysis as a truth-over-scale approach, using careful sampling to discover issues the team’s initial assumptions may miss.

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