Structured output is when an AI model returns its response in a predefined, machine-readable format, such as JSON that follows a specific schema, instead of free-form text. This lets software reliably read and use the result: saving fields to a database, triggering an AI workflow or filling a form. It is essential for turning generative AI from a chat tool into a dependable part of applications.
Key takeaways
- Structured output turns language model responses into data software can parse.
- JSON with a JSON Schema is the most common format.
- Methods range from prompt instructions to strict, enforced constrained decoding.
- It is closely related to function calling, where the model outputs structured arguments for a tool.
- A valid format does not guarantee correct content; values still need checking.
Structured output explained in simple terms
Asking an AI “what are the details of this invoice?” might return a friendly paragraph. That is fine for a person, but a program needs the details in fixed boxes: supplier name here, amount there, due date there. Structured output is like handing the AI a form with labelled boxes and requiring it to fill in only those boxes, every time, in the same format.
A worked example: invoice extraction
Schema (simplified):
- supplier_name: text, required
- invoice_number: text, required
- invoice_date: date (YYYY-MM-DD), required
- total_amount: number, required
- currency: one of “INR”, “USD”, “EUR”
- gst_number: text or null
Model output:
{ "supplier_name": "Sharma Office Supplies", "invoice_number": "INV-2041", "invoice_date": "2026-08-14", "total_amount": 18450.00, "currency": "INR", "gst_number": "27ABCDE1234F1Z5" }The application can now validate the GST format, check the total against a purchase order and save the record, without any text parsing. If the invoice has no GST number, the schema allows null, so the model does not have to invent one.
Ways to get structured output
| Method | How it works | Reliability |
|---|---|---|
| Prompt instructions | “Return only valid JSON with these fields.” | Moderate: may add extra text or break format |
| Few-shot examples | Show example JSON outputs | Better consistency |
| JSON mode | The API guarantees syntactically valid JSON | Valid JSON, but fields may not match your schema |
| Schema-enforced output | The API constrains generation to your JSON Schema | High: output matches the schema |
| Function / tool calling | The model returns structured arguments for a defined function | High, and designed for triggering actions |
| Validation and retry | Check output with a validator; ask the model to fix errors | Adds a safety net to any method |
Constrained decoding works by restricting which tokens the model may generate at each step, so only output matching the schema is possible. Several major AI providers now offer schema-enforced structured output in their APIs, and open-source libraries provide similar features for self-hosted models.
Structured output vs function calling
They are closely related. Structured output is about the format of the answer. Function calling (or tool calling) uses a structured format to request an action, such as “call get_weather with city = Pune”. Many systems use both: function calling to fetch data, and structured output to deliver the final result.
Designing good schemas
- Use clear, descriptive field names and short descriptions.
- Mark which fields are required and allow null where data may be missing.
- Use enums (fixed lists of allowed values) for categories.
- Specify formats for dates, currencies and IDs.
- Keep schemas as simple and flat as practical.
- Add a field for confidence or notes if reviewers need it.
Where structured output is used
- Extracting data from invoices, contracts, resumes and emails.
- Classifying and tagging content with fixed labels.
- Generating product catalogue entries in a set format.
- Powering agents that take actions in other software.
- Producing consistent reports and evaluations.
- Creating CSV or database-ready content, such as bulk publishing to a CMS.
Limitations
- A perfectly valid structure can still contain wrong values or hallucinated data.
- Over-strict schemas can force the model to guess instead of saying information is missing.
- Very complex, deeply nested schemas can reduce quality.
- Support for schema features differs between providers.