What Is Jev? How to Implement TypeSafe AI's Decision Model

A demo. Those are the values from TypeSafe's documented example for a similar message, and the guide itself calls its 0.9 urgency threshold illustrative, saying you would still need to test against labelled customer messages before production. One worked example shows the shape of the response, nothing about how often it is right.

No, and this constraint shapes designs. Questions are evaluated independently against the same state, in parallel, so if a second judgement needs the first result your application must send another request containing it. That means extra round trips and extra input tokens for genuinely sequential logic, so plan for it rather than discovering it in latency traces.

Treat it as an ordering hint. Score places the state on your ordered levels and returns a fractional position, so 1.035 sits between Calm and Frustrated, but the guide defines no unit, distribution or calibration for it. Notice the example's own threshold logic uses Noul's 0-to-1 probability instead, which is the primitive with a stated meaning.

pip install typesafe-sdk and a TYPESAFE_API_KEY environment variable, then a system_one call mixing Choice, Noul and Score. The caveats matter though: direct keys were in early access, the guide carries no clear publish date, and it reposts vendor and third-party figures while promoting the author's chargeback product. The code is usable; the numbers are borrowed.

Up to 255, answered in parallel inside a single Choice question. The guide notes that all three primitives can be combined in one request and that each question is evaluated independently against the same state, which is what makes a large option set practical.

No, and the guide says so. It points out that OpenAI's Structured Outputs already enforces supported JSON schemas through constrained decoding, so valid structured output alone is not new. The architectural difference it draws is that conventional frontier models generate output tokens sequentially.

Not one it ran. It sketches a chargeback-tool benchmark plan rather than reporting results, and the speed figures it repeats, such as 777 judgments under 0.7 seconds against 8.83 seconds, are borrowed from Every's testing rather than measured here.