jev.proIndependent unofficial Jev guide

Use cases· Last updated

Routing audit trail with Jev

Not every request deserves the same handler. Jev classifies cheaply; code sends work to a lookup, a specialist LLM, a costlier model, or a human.

This unofficial page is the audit trail slice of the intent and model routing pack. Intent: apply the Jev (TypeSafe System One) decision model to intent and model routing audit trail. Primary search language: Routing Jev audit trail. Confirm patterns on docs.typesafe.ai. This site does not sell, issue, or proxy TypeSafe keys. Use a credential you already have from the console or a documented gateway.

Independent angle (cover ≠ clone): Routing glossary + compare vs rules/queues; ranked-route floors. Not a clone of a rival intent-routing glossary page.

Routing use-case context

An audit trail for intent and model routing is a decision trace: replayable inputs, typed answers, floors, and the action the front-door router took. It is not a chat log and not a clone of a SIEM product page.

Hub: Intent routing. Compare, when the other tool is the real job: rules and queues.

Audit Trail inputs

Persist the filtered payload (the contract), not whatever arrived at the edge:

{
  "user": { "message": "Where is order 8831?", "locale": "en" },
  "session": { "authenticated": true, "plan": "free" },
  "catalog": { "handlers": ["order_status", "product_question", "return_exchange", "complaint", "other"] }
}

Redact secrets before the object hits cold storage.

Decision signals and actions

Minimum fields:

Also store usage.input_tokens (vendor meter) and the full probabilities map — argmax-only logs cannot explain a close intent.

Do not treat a Noul of 0.5 as a “medium” intent and model routing score — it means yes and no are equally likely. Conjunctions stay in your code.

Guardrails and escalation

If you cannot explain opening a refund/complaint LLM path or jumping to a costly model from the trace, you are not ready to auto-act. TypeSafe’s confidence-gated examples use a lower bar for recoverable reads than for irreversible actions. Those numbers are illustrations. For intent and model routing, treat complaint_auto_llm as the high bar (opening a refund/complaint LLM path or jumping to a costly model). Tune on labels — see offline evaluation.

Evaluation and rollout notes

Traces are the eval warehouse. Replay against handler gold and whether a human would have taken the complaint after criteria or alias changes. Pin jev-1.13.0 (the versioned id) after you fit thresholds. jev-latest and the marketing line jev-1.13 can move. Log the response model. TypeSafe’s published list price for jev-1.13 is $0.042 per million input tokens (vendor claim — confirm on the models page); output tokens are free on that same page. Unused distractors still bill as input.

Official Python and JavaScript SDKs read TYPESAFE_API_KEY and retry documented 429/529. This site does not sell, issue, or proxy TypeSafe keys. Use a credential you already have from the console or a documented gateway.

Pack map

Slice Page
Graph and primitives decision workflow
What may enter state input contracts
What to gather first evidence collection
Atomic rules policy checks
Act / review / abstain confidence thresholds
Reviewer payload human handoff
What to persist you are here
How it breaks failure modes
Labeled replay evaluation
Shadow → canary production rollout

FAQ

Is the HTTP log enough? No. Persist the filtered state, full probabilities, floors, and downstream action as a decision trace.

May I log raw secrets? Redact in code. Jev will not be your DLP layer.

Where is the rest of the Routing pack? Start with Routing human handoff and Routing evaluation. Cluster hub: Use cases.

Is intent a reserved API field? No. You name the key. See glossary: intent.

When do rules beat Jev? When the route is already in structured data. Compare traditional routing.

What this page does not claim

Disclaimer

This is an independent unofficial site and is not affiliated with TypeSafe AI; official documentation is available at https://docs.typesafe.ai.

Primary documentation: https://docs.typesafe.ai. Hub: Use cases.

Sources

Public TypeSafe or adjacent documentation only. No private claims.