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Many teams now use LLM-as-a-Judge to check AI answers, especially when exact-match tests fail for long or open-ended responses. But every judgement adds cost, delay, and possible bias, making this hard to scale.
Model routing can finally be a design choice rather than a system enhancement The post How to Build a Cheap, Yet Reliable Model Router With Jev appeared first on Towards Data Science.
Pydantic AI can run an agent on TypeSafe's Jev, a model that answers typed questions instead of writing text. Pydantic Logfire now shows each question, answer, and probability in Live view.
We moved bounded classification decisions in Ari from general-purpose LLM calls to TypeSafe's Jev, at roughly one-twentieth the cost and 10–15× the speed.
Bring your TypeSafe key to Pydantic AI Gateway and call Jev through the System One API, with metered cost, guardrails, and the same gateway key you use for every other model.
A short note on Jev's generalization, possible encoder-style architecture and training, and Choice and Noul API examples.
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