You already have a language model in production, and you are paying for it — four seconds per decision, three cents per call, and a schema-validation failure every now and then that quietly falls back to a human. The routing, the triage, the classification, the moderation, the re-ranking: none of it ever needed prose. It needed a value.
Jev is TypeSafe’s first System One model, and it is not a cheaper chat model. You hand it your application state — a string, a JSON object, a conversation — plus typed questions, and it returns typed values with calibrated probabilities in 70 to 500 milliseconds. There is no JSON to parse, because there was never a string. Hallucinated structure isn’t unlikely; it’s unrepresentable.
This course takes you from that idea to a system you can defend in a design review. You’ll learn the three primitives — Choice, Score and Noul — and how to pick between them without hesitating. You’ll learn why reading only the winning label throws away most of what the model told you, and how to gate real actions on calibrated confidence with a different bar for every blast radius. You’ll work through the five documented production patterns: speculative fan-out, confidence-gated routing, composite scoring, the cascade, and retrieve-then-judge — and how they compose without tangling your control flow.
