Skip to content
aiqbench takeAI Products

aiqbench analysis

This article details TypeSafe's Jev, a specialized AI decision model designed to map text inputs to predefined choices with calibrated probability outputs rather than generating free-form prose. By leveraging System One pattern-matching concepts, the technology aims to reduce hallucination risks in classification tasks like ticket routing while providing developers with confidence scores for each prediction.
Source coveringOpenRouter
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

What Is Jev? TypeSafe's Decision Model Explained for Developers

OpenRouter16 min read

Jev reads text and images and returns typed answers with probabilities instead of prose. Here's what that means, live API calls, how to read the numbers, and how to call it with an OpenRouter key.

What is Jev? And what should you use it for? It’s TypeSafe’s decision model that takes a passage of text plus a typed question, and returns a typed answer that is one of the choices among your predefined set of answers. So if I have a question about ticket routing, like should this ticket go to billing, technical, or account, Jev looks at the text of the ticket and answers with one of the predefined choices, billing. But it’s even cooler than that because it gives you the calibrated probability of the answer choice as well as the probability of each of the other choices. Oh, and there’s also an overall confidence score (more on that later). TypeSafe calls these System One models, and Jev is the first of them. But what does Jev actually return? Let’s find out! It returns three primitives, and there are real API responses for each below. Here’s how to read the probabilities without fooling yourself. And here’s how to call Jev with an OpenRouter API key. What is a decision model (System One model)? Jev is a decision model. A System One model is one that takes in a piece of state and, given…

Excerpt shown under the publisher's RSS terms · 200 words · full text and figures are on the publisher's site

Related coverage