How do Jev, chat models, and code work together?

One support message: who does which part?

A customer writes: “My parcel has not arrived. Can you check?”

An easy way to remember it: a chat model writes for people; Jev answers choices inside software. One workflow may use both.

Rules can classify too. Why consider Jev?

When a rule is simple and exact, use the rule. For example, code should compare dates for a policy that applies only within 30 days.

But people describe the same problem in many ways: “My package is late,” “Tracking has not moved,” or “When will it get here?” They may not share one keyword. You can try Jev to decide whether each is about delivery. Use your own examples to compare accuracy, speed, and cost before adopting it.

Can a chat model make the same choice?

Yes. Many generative models can also return answers in a required format. Jev is designed for repeated, fast decisions among defined options or scores, with probabilities. It does not write a complete reply.

Jev can still choose incorrectly. Your application must check permissions, handle uncertain results, and decide when a person should review a case. TypeSafe's headline speed and cost multipliers come from specific evaluations, not a guaranteed result for your workflow.

Want the details?

The next lesson explains Jev's three question types. For document selection, search can find candidates before Jev chooses among them; Jev does not replace your knowledge base or access controls.