# TypeSafe Jev: decision models, real benchmarks and limitations

LearnIA — teaching template — 2026-10-14

Use fictional data for the first trial. Adapt this template to your context and verify the result before using it.

## Brief to complete

- Objective and intended decision :
- Audience and context :
- Documents, versions and sources :
- Constraints and boundaries :
- Expected output format :
- Information to confirm :
- Reviewer :

## Starter instruction

Prepare a fictional-message routing pilot for billing, technical and human review. Define allowed options, ten ambiguous cases, human reference labels, critical failures and a calibration protocol with a separate validation set. Include a simple rule and a structured-output LLM as baselines. Make no calls and invent no scores.

## Quality review

- [ ] The publisher is identified.
- [ ] Schema validity and correctness are separate.
- [ ] Thresholds use separate validation data.
- [ ] Ambiguous cases can reach human review.

## Verification log

| Item | Source or calculation | Result | Correction |
| --- | --- | --- | --- |
| | | | |

## Approval and next step

- Remaining errors :
- Assumptions to resolve :
- Retained version :
- Next action and owner :

Full tutorial, example and sources : https://learn-prompting.fr/blog/jev-typesafe-modele-decision-benchmarks

This resource prepares a draft; it is not a certification, a grading decision, legal advice or a guarantee of results.