To make an LLM return JSON that your application can parse, configure the provider’s structured-output or JSON Schema response mode, then parse and validate the result in your own code. A schema can constrain the response’s shape; it cannot prove that the values are factually correct or valid under your business rules.
What structured outputs do—and do not—guarantee
Structured outputs are a provider feature that constrains a model’s response to a specified format, commonly a JSON Schema. They are useful when an application needs a predictable final payload for tasks such as extracting fields, classifying text, or returning structured data. Google documents these use cases in its Gemini structured-output guide.
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Conformance is about structure: for example, whether the response has the expected fields and types. It does not establish that a date was extracted correctly, a category is appropriate, or an amount satisfies a domain rule. Treat the model output as untrusted input even when the provider reports schema adherence. There is no reliability percentage in the cited provider documentation that would justify skipping application-side checks.
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Use structured output when the model’s final answer should have a defined format. Use function calling when the model needs to invoke a tool or take an intermediate action. These features serve related but different roles; Google describes the distinction in its guide to using tools with Gemini.
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Build a reliable JSON workflow
- Define a narrow schema. Specify the fields your application needs, use concrete types, and use enums when only a known set of values is acceptable. Avoid asking for fields your application will not use.
- Configure the provider’s constrained response mode. Supply the schema using the provider’s current API and SDK conventions. OpenAI’s API reference describes a
json_schemaresponse format with astrictoption and retainsjson_objectas an older JSON mode. Check the OpenAI API reference for the applicable request details. - Prompt for the intended content. State what each field should represent and provide clear instructions for ambiguous or missing information. A schema describes the shape, not the meaning or evidence standard for a value.
- Parse the returned content. Treat parsing as a separate step and handle cases where the response is absent, incomplete, or not usable as expected. Do not assume that a successful API request alone means your application has a usable payload.
- Validate meaning and invariants. Check values against domain rules: ranges, relationships between fields, allowed identifiers, required evidence, or other application-specific conditions. Reject or route questionable data rather than silently accepting it.
- Provide recovery paths. Handle refusals, incomplete responses, API errors, and schema incompatibilities explicitly. Depending on the task, that may mean asking for a corrected response, returning a controlled error, or requiring human review.
Expect schema limits to vary by provider
“JSON Schema” does not mean every provider supports every JSON Schema keyword or constraint. Google documents a supported subset for structured output, and OpenAI likewise limits strict mode to a subset. Design within the provider’s documented capabilities and test the exact schema and model you deploy; do not infer support for a keyword merely because it is part of the broader JSON Schema standard.
Provider implementations also differ in request configuration, SDK ergonomics, strictness controls, refusal and incomplete-output behavior, and how structured output interacts with tool calls. The cited documentation does not establish a complete, directly comparable provider-by-provider support matrix, so no provider can be declared categorically more reliable on that evidence alone. Verify current endpoint details, model availability, and schema constraints in the live documentation before deployment.
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A robust boundary is: constrain generation at the API, parse the returned content, validate its meaning in application code, and handle failures deliberately. Structured output makes downstream processing more predictable, but the application remains responsible for deciding whether a value is true enough, complete enough, and safe enough to use.
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