Jev vs LLM Structured Output

Separate schema-valid generation from a decision interface built around predefined answer spaces.

Two ways to obtain structured data

A structured-output LLM is a generative model constrained to a schema. Jev exposes a decision interface around Choice, Score, and Noul. Both can return objects your application can parse, but the way you specify the task and interpret uncertainty differs.

Compare the contracts

Concern	Jev	Structured-output LLM
Output space	Fixed categories, ordered rubric, proposition	JSON schema supported by the provider
Free-text fields	Not its generation role	Often supported
Uncertainty	Native probabilities; confidence for Choice/Score	Provider and implementation dependent
Validation	Still required	Still required
Semantic correctness	Must be evaluated	Must be evaluated

A valid answer can still be wrong

If a ticket about a technical outage is classified as billing, the label may be schema-valid and operationally wrong. Do not use parsing success as an accuracy metric. Keep labeled examples and check the actual downstream decision.

Prefer a bounded interface when the decision is bounded

If you only need one of four handlers, a Choice expresses that directly. If you need a paragraph plus extracted fields and citations, a generative model with structured output may better match the deliverable. Break apart the workflow when one interface is being stretched to serve two different jobs.

Migration checklist

Define the answer semantics first. Map types explicitly, preserve an unknown or review path, and compare outputs on identical input. Do not carry an LLM-derived threshold into Jev without evaluation; uncertainty values from different systems are not interchangeable.

Keep the executor independent

Authorization, numeric validation, idempotency, and confirmation must survive a model swap. If changing the classifier removes those protections, they were in the wrong layer. See agent risk gates for an example with an independent allowlist.