Mastering the Architecture of the Silent AI, JEV System One

When you use a chat-optimized LLM for classification, routing, and guardrails, it returns a string that you then have to parse again. This comes with hallucinations, type drift, delays of several seconds, and output-token costs. This course follows the architecture of Jev, TypeSafe’s first System One model: provide a state, and it returns typed decisions within a schema along with calibrated probabilities. The model does not generate strings. It covers, through concepts and code, why parallel sampling and RLCD (Reinforcement Learning for Calibrated Decisions) make those decisions fast, how to combine Choice · Score · Noul in a single request, and a hybrid approach that leaves structurally defined judgments to Jev and open-ended descriptions to an LLM.