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Home/How to set a Jev confidence gate: coverage and errors on six cases

EVALUATION / INTERACTIVE

How to set a Jev confidence gate: coverage and errors on six cases

Use six entirely synthetic tickets to understand the tradeoff, then validate with your own de-identified, human-labeled data. These numbers are not Jev API results and cannot select a production threshold.

These six records teach how metrics change. They cannot establish Jev accuracy, calibration, or an optimal gate.

3 / 6Auto-routed
3 / 6Human review
1 / 3Wrong among auto-routed
50%Auto-routing coverage

Illustrative confidence · ✓ Auto-routed · ○ Human review

  1. 01
    Synthetic case 01Human label: shipping · Illustrative prediction: shipping
    0.94
  2. 02
    Synthetic case 02Human label: billing · Illustrative prediction: billing
    0.89
  3. 03
    Synthetic case 03Human label: review · Illustrative prediction: shipping
    0.83
  4. 04
    Synthetic case 04Human label: shipping · Illustrative prediction: shipping
    0.76
  5. 05
    Synthetic case 05Human label: review · Illustrative prediction: billing
    0.69
  6. 06
    Synthetic case 06Human label: review · Illustrative prediction: review
    0.58

How to evaluate for real

  1. Write labeling rules for the correct team and whether automation is allowed. Keep ambiguous, multi-intent, and missing-data cases visible.
  2. Separate threshold-tuning cases from a locked final test set. Choose the gate only on the tuning set.
  3. Report coverage, wrong auto-routes, costly errors, review rate, failed requests, p50/p95, and total cost together.
  4. Repeat checks after model, option, or policy changes. Code permissions and human workflows still guard high-impact actions.
Download blank evaluation CSVTry the four-step practice
Further reading: Datawhale evaluation methods Datawhale Jev Cookbook ↗