Decision AI Models: TypeSafe Jev and OpenAI Decisions API

About this course

Learn AI decision making in Python with the OpenAI API (its new Decisions API) and TypeSafe Jev. These decision models do not write a paragraph back. They return a probability for every allowed answer, so your code can act on the number directly.

You use them for LLM classification (ticket triage), guardrails that check an LLM reply before it is sent, picking the right tool for AI agents, and grading photos. Structured outputs give you the right shape; a decision model also tells you how sure it is.

This course teaches two of them from zero, in Python: TypeSafe's Jev and OpenAI's Decisions API.You will:ask the three question types: yes or no (Noul / predicate), one pick from a list (Choice) and a rating on ordered levels (Score), many of them in one call;read probability and confidence, and turn them into an action: act, check first, or hand the decision to a person;find the weak spots before your users do: literal reading, long input, version names;test a vendor's claims with your own fair comparison on 300 tickets;build real projects: ticket triage, a reply guardrail, a tool picker, and a big project that merges duplicate company records into a knowledge graph;send photos to the Decisions API: grade egg photos, run a refund desk, and race a car on pictures to see why a cut-off is not the same as safety;send photos to Jev with its new image input and the new Box answer type;build a small web app with Streamlit, one tab per question type;decide when to use Jev, the Decisions API or a normal chat model, and when to use them together.

Every script, command and result in the course is a real run, recorded in real VS Code, and the full code is included as a download.

A note on dates. The course was recorded on 6 October 2026, when Jev's documentation listed it as text only.

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