How to Use the Jev AI Model: A Step-by-Step Developer Guide

Jev AI is presented as a decision-focused model for software applications that returns structured, typed outputs rather than open-ended chat responses, making it suitable for tasks such as ticket routing, risk scoring, human-review checks, and agent or model selection. Developers provide relevant context as text, JSON, or arrays of text, then ask bounded questions using Choice for predefined classifications, Score for ordered scales, or Noul for yes-or-no propositions expressed as probabilities. The guide recommends starting with a narrow, low-risk decision, testing it in the Playground against representative and ambiguous examples, and then integrating the server-side API with securely stored credentials. Responses include selections, probability distributions, confidence signals, and usage data, but application code remains responsible for executing actions and enforcing permissions. It emphasizes evaluating thresholds with historical data, using safe fallbacks for errors or uncertain cases, minimizing unnecessary input data, logging decisions responsibly, and requiring human approval for high-impact actions.

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