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The following article originally appeared on Arize’s blog and is being reposted here with the author’s permission. This week the AI community was in uproar about Jev from TypeSafe, not just a new model but a new kind of model: one that classifies, scores, and routes but can’t write a sentence.
Use Jev for categorical judgments, probabilities, and numeric rubric scores in Pydantic Evals, then track the results in Logfire without a per-score fee.
What is Jev? Learn how TypeSafe AI’s System One model makes fast, structured decisions, where it fits in the agent loop, and how to use Jev with LangChain
We have an unusual relationship with today’s guest: for years since coauthoring the InstructGPT paper, Diogo Almeida had been saying that API-available frontier models have been going down the wrong path, everything from the alignment to refusals to reliability perspectives, that we have dropped…
Jev reads text and returns typed answers with probabilities instead of prose. Here's what that means, three live API calls, how to read the numbers, and how to call it with an OpenRouter key.
Jev is a new kind of AI model from TypeSafe that answers typed questions with probabilities instead of writing text. What I found testing it, the one prompting lesson worth knowing, and how Respira AER 0.3 uses it to check every change to a WordPress site.
We analysed 12,759 tweets about Jev's launch to separate TypeSafe AI's speed and cost claims from users' reported results and see where a decision model fits.
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