This course uses AI-assisted production, including text-to-speech narration and AI-assisted visual/content workflows. All course material, experiments, technical explanations, results, and learning resources were reviewed and curated by the instructor. Large language models are powerful at understanding meaning, but production software needs decisions that are structured, testable, measurable, and controllable. Jev for Developers teaches you how to build reliable AI decision workflows using Jev, TypeSafe AI’s System One model for typed probabilistic decisions. You will begin with Jev’s core primitives—Choice, Score, and Noul—and learn how to turn semantic judgments into bounded outputs with probabilities and confidence.
From there, you will design compact state, write atomic semantic questions, fan one request out into multiple independent judgments, and combine those signals using deterministic application logic. The course then moves into confidence-aware routing, consequence-based policy gates, human-review paths, and practical guardrails. You will also examine adversarial inputs and learn why checking only the final winning label can miss important changes in individual signals. In the evaluation module, you will follow a real design progression from broad, overloaded judgments to smaller, testable signals. You will work with a frozen 20-case regression dataset, analyze taxonomy disagreements, inspect confidence behavior, and learn how model, question, and policy changes can be regression-tested.
