Structured outputs
Also known as: structured output, schema-constrained output
Structured outputs is a feature that makes an AI language model produce responses in a precise, machine-readable format, usually JSON that matches a schema you define, with exact field names and data types.
That lets other software use the AI's answers without fragile parsing. While structured outputs guarantee the format, they don't guarantee the content inside is correct, so values still need checking.
Structured outputs, explained at three levels
At home
Imagine asking someone for directions and getting a rambling story, versus getting a filled-in form with boxes for 'street,' 'turn,' and 'distance.' Structured outputs make an AI fill in the form. You define the boxes in advance, and the AI is forced to answer in exactly that shape, so another app can read the answer without guessing where each piece of information is.
At work
Structured outputs is an AI API feature that forces a model to reply in an exact machine-readable format, usually JSON matching a schema you define, with set field names and data types. That makes AI answers safe to feed straight into other software. Businesses use it to extract data from emails, invoices, and forms into accounting systems, CRMs, and spreadsheets without fragile parsing code.
At home
You describe the shape you want, such as a list of grocery items, each with a name, a price as a number, and a date. That description is called a schema. Behind the scenes, the AI writes its answer one token at a time, and the system blocks any token that would break the shape, like a missing bracket or a word where a number belongs. So the result always fits the shape.
At work
Developers supply a JSON Schema, and the provider converts it into a grammar. During generation, constrained decoding masks out every token the grammar wouldn't allow at that point, so the output is guaranteed to parse. Major providers such as OpenAI, Google, and Anthropic offer this natively, and open tools such as Outlines, llama.cpp grammars, and XGrammar do the same for self-hosted models. Complex schemas can add latency on first use, and some schema features may be unsupported.
At home
Constrained decoding controls which tokens are allowed, not whether they're true. If the model isn't sure about a total, the schema still forces it to write some number, and that number will look just as tidy as a correct one. While the format is guaranteed, the facts inside are not. In my opinion, that's the trap: neat formatting makes errors look trustworthy, so I'd still check the numbers.
At work
Schema design shapes accuracy. Allow null or 'unknown' values so the model isn't forced to invent missing data, and validate values against business rules after extraction, such as line items summing to the total. Very strict constraints can reduce reasoning quality, so some teams add a reasoning field before the answer fields, or let the model think freely and format last. Version schemas like APIs, and evaluate field-level extraction accuracy on real documents.
Real-world examples
At home
Marcus built a tool that reads his grocery receipts with AI. Structured outputs make it return each item, price, and date in the same format every time, so his budgeting spreadsheet updates automatically.
At work
Fatima automates accounts payable at a Calgary construction firm. She used structured outputs to pull vendor, invoice number, and total from emailed invoices, with a rule-based check flagging totals that don't add up.
How it compares
| feature | Structured outputs | Asking for JSON in a prompt |
|---|---|---|
| Format reliability | Guaranteed to match schema | Usually, but can break |
| Setup | Define a schema | Write instructions |
| Content accuracy | Not guaranteed | Not guaranteed |
| Parsing code | Simple | Needs error handling |
Structured outputs fix the format, not the facts.
For analysts
When automating data entry with AI, I'd define the schema with business users and add validation rules. The format will be right; the values still need checks before they hit the ledger.
History & origin
Early developers asked models to 'reply in JSON' and hoped for the best, often getting broken brackets or extra commentary. Function calling, introduced by OpenAI in June 2023, made structured responses more reliable. Open-source libraries such as Microsoft's Guidance, Outlines, and llama.cpp's grammar support used constrained decoding to block invalid tokens during generation. OpenAI added a JSON mode in November 2023 and launched Structured Outputs with strict schema adherence in August 2024, and other providers added similar features. In my view, structured outputs turned language models from chat partners into dependable software components.