What are structured outputs?
Structured outputs are responses from a language model that follow a format the developer specifies in advance, most often a JSON object that matches a declared schema. Instead of a paragraph that says the invoice is from a certain vendor and is due at the end of the month, the model returns named fields: vendor, amount, currency, due date. Software can then read those fields directly, validate them, and write them to a database.
There are two ways to get them. The weaker way is to ask in the prompt for JSON and hope the model complies; it usually does, but it sometimes adds commentary, misnames a field, or produces invalid syntax. The stronger way is constrained decoding, where the inference server restricts the model’s token choices at each step so that only output valid under the schema is possible. Most hosted APIs and open-source inference servers support this directly, and it is the same machinery that makes function calling reliable.
Why structured outputs matter
Structured outputs are what turn a chat model into a component that other systems can depend on. An agent that must call tools, an intelligent document processing pipeline that fills an ERP record, or a classifier that routes tickets all need output a program can read every time. Without a schema, developers write brittle parsing code and handle a long tail of formatting surprises. With one, the failure modes shrink to wrong values, which data validation can catch.
Structured outputs in practice
A freight brokerage receives carrier invoices and bills of lading as PDFs and emails in dozens of layouts. A model reads each document and returns a fixed schema: shipment reference, origin, destination, weight, accessorial charges as a list, and a confidence field for each value. A validation step checks that the shipment reference exists in the transportation management system and that the charges match the rate confirmation. Records that pass are posted automatically, and the rest go to a clerk with the mismatched fields highlighted.
What to watch for
- A schema guarantees shape, so a result can be perfectly formatted and still wrong; keep validation rules and a confidence threshold.
- Overly complex schemas with deep nesting or many optional fields reduce accuracy, so split the task into smaller extractions if quality drops.
- Confirm whether your inference server enforces the schema or only requests it, because the two fail very differently in production.
- Give the model an explicit way to say a field was not found, or it will fill the slot with a guess.