---
title: "Structured output"
slug: "structured-output"
category: "glossary"
tags: ["glossary", "ai-agents", "structured-output", "json", "schema", "validation"]
status: "stable"
last_updated: 2026-10-01
aliases: ["glossary/structured-prompt", "glossary/schema-validated"]
summary: "Structured output makes a model return data matching a JSON Schema; use schema-constrained decoding when code reads the response, and still validate values."
related: ["[[ai-agents/structured-output]]", "[[prompt-engineering/output-constraints]]", "[[glossary/tool-use]]", "[[glossary/few-shot-prompting]]", "[[glossary/hallucination]]", "[[ai-agents/evaluation]]"]
---

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## Overview

Structured output makes a model return data that matches a schema, usually JSON Schema, so code can parse it without regex extraction. There are three levels. A prompt that asks for a format (a "structured prompt") is weakest; the model can still deviate. JSON mode guarantees valid JSON but not your schema, and OpenAI recommends structured outputs instead. Schema-constrained decoding guarantees the schema: Claude uses `output_config: {format: {...}}` (the older top-level `output_format` is deprecated) and `strict: true` on a tool definition for schema-valid tool input; OpenAI uses `text.format` with `type: "json_schema"` and `strict: true`. Open-weights runtimes get the same guarantee from grammar-constrained decoding (llama.cpp grammars, Outlines). Use the strongest level whenever a program reads the response.

Validate anyway. A valid schema does not make the values right. Check business rules the schema cannot express (`start_date < end_date`, `quantity > 0`) with Pydantic or Zod where the response enters your code, and on failure retry once or log, never silently fill a default. Keep schemas small, with few required fields.

On Claude 4.6 and later models, assistant-message prefill returns a 400, and so does forced `tool_choice` on Fable 5.1, Opus 5.5, and Sonnet 5.5; use structured outputs instead. Citations cannot be combined with `output_config.format`.

## Example

```json
{
  "type": "object",
  "properties": {
    "priority": { "enum": ["P0", "P1", "P2", "P3"] },
    "needs_human": { "type": "boolean" }
  },
  "required": ["priority", "needs_human"],
  "additionalProperties": false
}
```

```python
ticket = Ticket.model_validate_json(response.content[0].text)  # Pydantic; raises if invalid
```

## Related

- [[ai-agents/structured-output]]: schema design and provider differences.
- [[prompt-engineering/output-constraints]]: prompt-level format control.
- [[glossary/tool-use]]: tool arguments are structured output too.
- [[glossary/few-shot-prompting]]: examples that show the format.
- [[glossary/hallucination]]: validation catches fabricated values.
- [[ai-agents/evaluation]]: exact-field scoring makes these cheap to evaluate.