After labeling a batch, I talk the labels through with Claude Opus to pin down what good looks like, then let it label the rest. Then we run Jev against the golden dataset: 88% correct. Why that's fine for this task, why 99% on the first try is a warning sign, and how this makes switching models easy.
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