Every app makes thousands of small decisions. Is this ticket urgent? Which team owns it? Is this message safe to send? Which tool should my AI agent call? For years we answered these with brittle rules or with slow, costly chat models that sometimes return broken JSON. In September 2026, TypeSafe AI released Jev, the first System One model. It does not chat and it does not write text. You give it an input and a few typed questions, and it returns typed answers with probabilities in a single fast pass. Within weeks, a whole family of decision models followed, including Laya, an open model that runs on your own laptop. This course gives you a clear, beginner-friendly understanding of decision models: what they are, how they work, and where they fit.
What you will learn:
- What a decision model is, who built Jev and why, and how it differs from a chat model
- The three question types, Choice, Score and Noul, and when to use each one
- How to read probabilities, confidence and calibration, and how to set thresholds from the cost of a mistake
- How decision models are trained: RLHF vs RLVR vs RLCD
- Four production patterns: speculative fan-out, confidence-gated routing, composite scoring and intent routing
- 10 real use cases, from helpdesk triage and prompt injection guards to RAG filtering and payment risk
- How decision endpoints differ from the chat completions endpoints you already know
- The landscape of closed and open decision models, and the SDKs, gateways, frameworks and local runtimes that support them
- Where Jev gets things wrong, and the habits that make decision models reliable
