JJEV Field Guide
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JEV / LESSON 08 · 12 min

From Jev demo to deployment: examples, thresholds, and fallback

Use a reproducible evaluation sheet to inspect costly errors, review rate, and end-to-end cost.

Source checked 2026-09-24Independent learning site. Not affiliated with TypeSafe AI.

Define a complete outcome

For support routing, the workflow reads a ticket, fetches facts, builds state, calls Jev, validates an answer, routes or reviews, and records the outcome. List failure paths for every step.

Create an example sheet

Keep de-identified state, human label, scenario, candidates, model version, prediction, reported probability, actual action, review flag, and error cost. Separate common cases from costly long-tail cases.

Tune using error costs

A wrong refund route and an extra human review have different costs. Choose thresholds on labeled data and verify them on a held-out set. Missing fields and API errors need explicit fallback.

Measure the whole workflow

Report routing accuracy, severe mistakes, review share, end-to-end p50/p95, cost per thousand items, and user experience. Compare an LLM using the same cases and a sensible structured-output setup.

Roll out and recheck

Start with read-only or reversible actions and sampled review. Log model version and request ID. Re-evaluate after model, candidate, or policy changes; fall back when errors rise.

Copyable sheet header

case_id, locale, scenario, state_redacted, expected_label, predicted_label, confidence_or_noul, model_version, action, human_review, error_cost, latency_ms. Do not store API keys or unnecessary personal data.

Download a blank evaluation sheet

The CSV contains headers only, with no user data or model output. Add your own de-identified cases, compare labels, and inspect the action and review fields.

CSV ↓

Practice

Practice

You tuned a threshold on labeled cases. What next?

This is a knowledge exercise and does not call Jev. The explanation is instructional.

What does the support team actually need to decide?

Follow one support ticket from raw text and verified facts through three questions, an illustrative answer, edge cases, and launch evaluation.

Worked example: design a Jev workflow for a missing delivery

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