What is the Jev model?

Jev accepts a state and named questions. Each question defines an answer shape in advance: a choice among labels, a score on an ordered rubric, or the probability of a proposition. The result is data your code can inspect instead of a paragraph you must interpret.

Suppose a customer says that a payment failed before an important deadline. Send that message as state. Ask a Choice question to select the billing or technical queue, a Score question to rate urgency on a written scale, and a Noul question about whether a person should review the case now. Each answer has a different job; your code can combine them with your own policy.

Use Jev when the possible outcomes are known and the next step can be expressed in code. Keep open-ended writing, complex plans, and explanations with a generative model. A Jev result is a signal; your application still owns thresholds, permissions, and real-world actions.

Open the workbench on the homepage, choose a preset, and replace its sample state with a non-sensitive example of your own. Read the question definitions before running it. Compare clear and ambiguous cases, then inspect the returned probabilities instead of treating the chosen label as a guarantee.