Jev Fundamentals: Fast AI Workflows & Routing

LLMs are great at writing, but they are a poor fit for the small, fast decisions inside your application code: which queue a ticket belongs in, which model should reply, whether a refund needs human approval. They return labels without reliable confidence, their self-reported scores can’t be trusted, and every decision costs output tokens.

Jev is a System 1 model built for exactly these decisions. Think of it as a smart if statement: you give it state and questions, and it returns calibrated probabilities you can route on, quickly and cheaply.

In this course you will learn:

• Why LLM labels and confidence scores break down for routing (and the role RLHF plays)
• How Jev works as a System 1 model, and how it compares to LLMs and traditional ML classifiers
• How to call Jev with state and questions using choice, score and Noul question types
• How to write good questions, read calibrated probabilities and build confidence-gated routing
• How Jev’s pricing and speculative fan-out cut cost and latency
• Where Jev is a bad fit and how to tell calibration apart from confidence

Hands-on demo: set up TypeSafe and KodeKey, connect Claude Code, build a small app and compare Jev head-to-head with GPT-5.5 and DeepSeek.

Project: design a Jev-powered customer support agent that picks which LLM replies (Haiku vs Opus), requires human approval for refunds, checks replies against policy, and rolls out safely in shadow mode. You finish with a hands-on lab where you send your first ticket to Jev.

By the end, you will know when to reach for Jev, when to let your own code decide, and how to add fast, reliable AI decisions to your applications.