[100% Off] Jev: Fast, Typed Ai Decisions With System One Models
Swap slow, hallucinating LLM calls for sub-second typed decisions in routing, triage and guardrails
Requirements
- Comfortable writing Python (3.10+)
- You have called an LLM API before
- A TypeSafe early-access API key for the hands-on labs
- An OpenAI or Anthropic key for the cascade labs
Description
This course contains the use of artificial intelligence.
You already have a language model in production, and you are paying for it — four seconds per decision, three cents per call, and a schema-validation failure every now and then that quietly falls back to a human. The routing, the triage, the classification, the moderation, the re-ranking: none of it ever needed prose. It needed a value.
Jev is TypeSafe’s first System One model, and it is not a cheaper chat model. You hand it your application state — a string, a JSON object, a conversation — plus typed questions, and it returns typed values with calibrated probabilities in 70 to 500 milliseconds. There is no JSON to parse, because there was never a string. Hallucinated structure isn’t unlikely; it’s unrepresentable.
This course takes you from that idea to a system you can defend in a design review. You’ll learn the three primitives — Choice, Score and Noul — and how to pick between them without hesitating. You’ll learn why reading only the winning label throws away most of what the model told you, and how to gate real actions on calibrated confidence with a different bar for every blast radius. You’ll work through the five documented production patterns: speculative fan-out, confidence-gated routing, composite scoring, the cascade, and retrieve-then-judge — and how they compose without tangling your control flow.
Then we get practical about running it. Async clients and real concurrency. Rate limits, and the genuinely sneaky way they fail — your latency climbs while your error dashboard stays green. Cost engineering, where trimming state makes the system cheaper and more accurate at the same time. Latency budgeting inside a one-second request. And an honest tour of all nine documented failure modes, because a model that cannot do arithmetic or compare dates is a model you should design around deliberately.
Every section builds on one running example: Fernway, a 410-person SaaS company routing twelve thousand support tickets a month. By the final section you’ll have built their triage autopilot end to end — and reported what it costs per ticket against the all-LLM baseline, because being able to state that number is what separates a demo from something a business adopts.
Jev entered early access on 21 September 2026. Every technical claim here is sourced from the live documentation, and the course tells you exactly which numbers to re-verify as the model moves.
Author(s): Dr. Amar Massoud
