Why Your AI Is Confidently Wrong, and What You Can Do About It
TL;DR: Fluent AI output is not necessarily correct. In this project, I built a lightweight Python workflow that uses explicit scoring rubrics, structured human annotations, Pydantic validation, evaluator-agreement checks, and an adjudication queue. On the included synthetic example dataset, the pipeline compared 15 shared cases, reported 86.7% exact error-label agreement and 93.3% exact severity…
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