Reduced my Jev judge’s calibration error [D]

A developer reduced their Jev judge's calibration error by 68.1%, from an ECE of 0.0982 to 0.0313, after learning from human-labeled examples on the TRIVIA+ dataset. While hallucination-detection F1 only slightly improved from 0.5833 to 0.5877, the judge's confidence became more aligned with reality. This distinction is crucial for applications where confidence scores directly trigger actions, highlighting the importance of calibration in Typed Evals rather than relying on raw judge confidence.

This report uniquely details a 68.1% reduction in calibration error for a Jev judge, unlike other metrics like F1 score, which saw only a minimal change.