Structured Output

What is structured output?

By default a model hands you natural-language text. Fine for humans, annoying for machines — ask it to “summarize as JSON” and if you're unlucky you get prose that merely looks like JSON. Structured output uses an explicit format contract, most often JSON Schema, to tell the model exactly what the fields look like, what types they are, and which are required, so it generates something a program can parse directly.

Why do you need it?

It plugs AI into a workflow
The output has to feed the next program, land in a database, or go to a frontend — and that requires a stable format. Free text can't be automated.
It cuts parsing errors
“Get free text, then scrape fields with regex” is fragile and fiddly. Having the model emit compliant data directly removes layer after layer of patches.

How is it done?

Prompt conventions
The simplest route: spell out the format in the prompt and give examples — though it isn't 100% reliable.
Constrained decoding
The hardcore approach limits which tokens can be generated using the Schema at sampling time, killing format errors at the root.
Function calling
Many platforms tie structured output to tool calls: the model just fills in parameters, and the framework guarantees the format.

Things to watch

The tighter the constraint, the less room the model has, and content quality can suffer. Design fields sensibly: leave the model the free-text space it needs instead of sacrificing the answer for a pretty format.

Bottom line: structured output makes the AI's answer “born with a format” — valid the moment it's generated, usable the moment you get it.