A concrete example
Instead of a paragraph, the AI returns {"name":"...","email":"...","score":82}.
Why it matters
Workflows break fast when the format changes. Structured output reduces that risk.
You run into it in data extraction, automations, agents wired to a database, and generated forms.
Don't mix it up with
Prompt engineering: Prompt engineering means phrasing a request with enough context, examples, and constraints to get a useful answer.
Evaluation / Eval: An eval regularly measures whether an AI answers correctly on representative cases.
Common mistakes
- Asking for JSON without validating the result.
- Using vague fields.
- Forgetting the case where the AI doesn't have the information.
Quick checklist
- First I check whether the word names a concept, a tool, a risk, or a metric.
- I tie it to a concrete case: Instead of a paragraph, the AI returns {"name":"...","email":"...","score":82}.
- I keep the main trap in mind: Asking for JSON without validating the result.
Quick questions
What is Structured output in AI?
Structured output forces an AI to reply in a precise format, often JSON, so a tool can reuse it.
Where will I run into Structured output?
You run into it in data extraction, automations, agents wired to a database, and generated forms.
Which word should I read next?
Start with Prompt engineering, Evaluation / Eval, Tool use / Function calling.