10 Steps to Use Jev in Real Agents with Verifiable Numbers

An article by Nokka provides a 10-step guide on using Jev, a model from TypeSafe AI, in real-world agent systems. Jev is designed to make decisions by evaluating options and their probabilities, rather than generating text. The guide emphasizes separating decision-making from action, and provides best practices for integrating Jev, such as setting up a state with relevant data, asking atomic questions, and using confidence scores to determine when to trust Jev's decisions.

The article also cites several case studies demonstrating Jev's performance, including applications in browser automation, document evaluation, and mobile device control. TypeSafe AI has published a cookbook with experimental results, showing that batching questions can significantly reduce costs and improve performance.