What Are Structured Outputs?
Structured outputs let you force an AI model to respond with valid JSON that matches a schema you define. Instead of getting free-form text that you have to parse and hope is formatted correctly, you get guaranteed-valid data structures every time.
OpenAI introduced this as “JSON mode” and later expanded it into full structured outputs with schema enforcement. You pass a JSON Schema alongside your prompt, and the model’s response is constrained to match that schema exactly. No missing fields, no extra properties, no type mismatches.
Claude handles this differently – through its tool_use system. You define a “tool” with an input_schema, and Claude returns structured data matching that schema. The end result is the same: predictable, parseable JSON output.
Why Schema Validation Matters
A schema that looks right isn’t necessarily valid. Common mistakes include forgetting the type field on properties, using required as a property-level keyword instead of an array on the parent object, or defining an enum with mismatched types. These issues won’t throw errors when you write the schema, but they’ll cause unexpected behavior when the model tries to follow it.
This tool catches those problems before they hit production. Paste your schema, and it’ll flag structural issues, missing fields, and type inconsistencies. It’s faster than finding out through a confusing API response at 2 AM.
From Schema to TypeScript
If you’re building in TypeScript (and let’s be honest, most API integrations are these days), you want type safety for your AI responses. Writing TypeScript interfaces by hand from a JSON Schema is tedious and error-prone.
The TypeScript tab in this tool converts your JSON Schema into a TypeScript interface automatically. String properties become string, numbers become number, arrays get typed, and optional fields get the ? modifier. Copy the generated interface into your codebase and you’ve got end-to-end type safety from schema definition to response handling.
Generating Example Responses
Sometimes you need to see what a valid response would actually look like before wiring up the API. The “Example” tab generates a sample JSON object that conforms to your schema, using realistic placeholder values. It’s useful for testing downstream code, writing unit tests, or just sanity-checking that your schema produces the shape of data you expect.
Common Schema Patterns for AI
A few patterns work particularly well with structured outputs:
Flat response objects. Keep it simple when you can. A schema with 3-5 top-level properties is easier for the model to fill correctly than deeply nested structures.
Enum-constrained fields. When a field should be one of a few specific values (like "sentiment": "positive" | "negative" | "neutral"), use enums. The model adheres to them perfectly.
Arrays of typed objects. Need the model to extract a list of entities? Define an array with items pointing to an object schema. Works reliably for extraction tasks.
Optional fields with defaults. Mark fields as optional when the information might not be present in the input. The model will omit them cleanly rather than hallucinating values.
For building function calling schemas (which use the same JSON Schema format), check out our JSON Schema Generator.