Walk a Surprising Score Through PDCA
Diagnose an unexpected AI benchmark result through Plan, Do, Check, and Act.
A translation model drops from 89 to 76 on Friday's benchmark. The product channel fills with panic. Maya has to decide whether the model regressed, the test changed, or the score is noise. Use PDCA to separate model change from benchmark change. Plan the hypothesis, Do a controlled rerun, Check slice-level evidence, Act only on the isolated cause. Plan Do Check Act Try What should Maya write before rerunning anything? What controlled check comes next? The fixed holdout is stable, fresh OCR-heavy PDFs dropped. What does that imply? What action fits the evidence? Now you try A judge model update…
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