Structured Outputs · Product Fluency

Structured Outputs · 3 min

Situation

The answer is readable but hard to integrate.

An AI classifier replies, “This looks like a billing issue, probably urgent.” Your software needs a category and a review flag. Parsing arbitrary prose creates brittle branches.

A structured output gives the application a predictable shape to consume. It does not eliminate the need to evaluate whether the classification is right.

Mental model

A schema defines the shape of the result.

A schema can require fields, specify types, and restrict allowed values. Supported structured-output features can enforce that schema for completed outputs under their documented conditions.

Plain JSON mode and a prompt asking for JSON are not always equivalent to schema enforcement. Confirm the model and provider's actual behavior, including refusals and incomplete responses.

Example

Use fields that reflect a product decision.

{
  "category": "billing",
  "needs_review": true,
  "reason": "The charge is not identifiable."
}

The category could be restricted to a known set. needs_review is a boolean. The application can route the result without guessing where the category appears in a sentence.

Failure case

Valid JSON can contain a wrong decision.

The model may output an allowed category that does not fit the ticket. It may produce a date string that passes a format check but refers to the wrong date.

Validate business rules and permissions separately. Handle refusal, timeout, missing input, and interrupted generation explicitly rather than treating every API response as a usable classification.

PM question

What should happen when the input does not fit?

Include a designed path for ambiguity, unsupported categories, or absent evidence. A schema that forces every ticket into a narrow label can hide uncertainty.

Keep the shape as simple as the consuming workflow needs. More fields create more things to evaluate and can invite unsupported precision.

Remember this

Structure makes integration easier; evaluation makes it trustworthy.

Use predictable data contracts, then test semantic accuracy and exception behavior. A parsable answer can still be unsuitable for action.