Developers / JevModel
Evaluate a Jev workflow
Test labels, uncertainty, and real outcomes before automation.
Build a small labeled set
Collect representative states from the actual workflow, including ambiguous and rare cases. Have reviewers label the intended branch independently. Keep test states separate from examples used to tune instructions.
Measure decisions, not just outputs
Check accuracy by class, calibration by probability band, fallback rate, and downstream outcomes. Choose thresholds based on the cost of each error. Re-evaluate when the question wording, model version, or input distribution changes.
JevModel is independent and not affiliated with TypeSafe AI.