Developer Finds Costly Grading Bugs Skewing AI Model Evaluations Across Three Projects

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A developer running an AI training platform discovered that evaluation bugs — not model weaknesses — were responsible for poor performance scores across three separate business projects in the same month. In one case, a frontier model scored identically to smaller open-source models on a 54-task exam, revealing that 34 tasks were broken and leaking evidence between questions. A healthcare denial-management engine appeared to show only 44.7% agreement with a reference agent, but a manual review found the eval was using inconsistent scoring rules; corrected metrics showed over 90% accuracy. A third project involved a Medicare compliance platform where the grading system was controlled externally, presenting a different but related challenge. The core lesson across all three cases was that evaluation code carries its own bugs, and flawed graders silently distort what 'passing' means — making them among the most consequential errors in AI development.

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