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Jev or an LLM: start with the output you need

Use a fixed decision for routing and scoring; use generated text for replies and explanations.

2 MIN READ · UPDATED 20 SEP 2026
Your outputStarting point
A team name or fixed categoryTry Choice
A rating on defined levelsTry Score
A yes/no probabilityTry Noul
A reply, explanation or generated codeUse a text-generating model

A label and a reply are different jobs

Consider a support inbox. Choosing shipping or billing produces a label. Writing an answer about a delayed order produces text. Jev fits the first task; a text-generating model handles the second. Your application may need both.

Do you need a separate model call?

If the text model already reads the ticket and produces a usable category with its reply, another call adds work. A separate decision step is more interesting when it can send the request to code, skip an expensive reply or choose between handlers.

Try one decision in the existing workflow

Keep the current system as a baseline. Replace only its category or score step, using the same inputs and review policy. Record whether the final task succeeds, along with response time and billed usage. A fast category decision is not useful if it causes a second round of correction.

Choose based on what changes for the user

Keep the simpler system if the extra decision step does not improve the result. Consider the split when its measured errors, review workload and response time meet your requirements. There is no model-wide winner independent of the task.

Related official reading

Official workflow evaluationsThe publisher evaluates four workflows against model-consensus reference answers. Agreement with those references is different from accuracy on an independently labeled test set.