Structured outputs

We want artificial intelligence (AI) to give answers we can use, not puzzles we have to untangle. That’s where structured outputs come in. Think of them as guardrails so she doesn’t wander into a ditch of vague text.

Why structure matters

Plain text is messy. We can’t reliably parse a date or an address if she decides to be poetic. Structure means wrapping the answer in a predictable format—usually JSON. Once it’s predictable, we can test it, store it, or feed it to another system without babysitting.

JSON schemas

A schema is just a recipe. It tells her what ingredients belong in the response and what’s out of bounds. Example:

{
  "name": "string",
  "age": "integer",
  "email": "string"
}

If she tries to slip in “banana” for age, validation rejects it. Simple. Effective.

Validation as a safety net

Validation checks the response against the schema before we trust it. Without it, we’re the unlucky tester who discovers too late that “42” showed up where an email should go. With it, we get a quick thumbs-up or an error we can handle. The machine doesn’t guess—we decide what’s valid.

Working with her

She’ll happily try to follow rules, but she’s not infallible. We still need to check. Structured outputs let us keep the conversation tidy while avoiding those oddball surprises that creep in when we just accept whatever she says.

A coder’s musing

We’ve learned the hard way that humans improvise well but machines fake it poorly. Schemas are our way of saying: improvise less, deliver more.