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An LLM decision API returns a typed object—such as a category, amount, or action choice—that an application can read directly, instead of asking the application to extract a decision from a paragraph. Structured output can make the response easier to parse, but it does not prove the decision is correct or safe to act on.
What it means for an API to return values, not text
In this pattern, the application defines an output contract: named fields, expected types, allowed values, and rules for representing missing or ambiguous information. The model returns data shaped to that contract, which the application can inspect and pass to its own logic.
For example, a support workflow might request a category and a priority rather than a free-form explanation. The application can then validate those values and decide what to do. The distinction is architectural: “LLM decision API” describes a way to use an LLM, not a universal API product or established standard by that name.
OpenAI describes structured response formats as a way to shape a model response, while function calling is for connecting the model to application functions, tools, or data. See OpenAI’s Structured Outputs guide.
Choose the right output mechanism
| Mechanism | What it provides | Best fit |
|---|---|---|
| JSON mode | Valid, parseable JSON, but no guarantee that the output follows a particular schema. | When JSON syntax is useful but strict field-by-field conformance is not required. |
| Structured Outputs | Output constrained to a supplied supported schema. | When the application needs structured data in a defined shape. |
| Function calling | A model-selected call to an application function or tool, with arguments shaped by its definition. | When the model needs to fetch data, perform computation, or request an application action. |
These are not interchangeable guarantees. OpenAI’s Help Center states that JSON mode ensures valid JSON but “will not guarantee the output matches any specific schema, only that it is valid and parses without errors.” Read the OpenAI Help Center explanation of function calling alongside the function-calling guide when choosing an interface.
Define the contract before writing the prompt
A schema is useful only if it expresses what the caller needs to receive. Specify the fields and types, which values are allowed, and what the model should return when it cannot make a decision. For example, use an explicit “unknown” or “needs_review” state when appropriate rather than letting the application interpret an absent or improvised value.
- Make required fields and allowed values explicit.
- Define how uncertainty, missing details, and conflicting input are represented.
- Keep the output focused on data the application actually consumes.
- Check that the selected model, API endpoint, and schema features support the behavior you rely on.
Strict function calling also has schema requirements, including marking fields as required and setting additionalProperties to false. Supported JSON Schema features and model compatibility vary; consult current function-calling documentation rather than assuming every schema works unchanged.
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Schema validity is not decision correctness
A schema can constrain the response’s shape without establishing that its values reflect the user’s intent or satisfy business rules. A field may contain an allowed value that is still the wrong choice. Before a returned value triggers a purchase, booking, account change, or other consequential action, validate it against authoritative application data, policy, permissions, and the user’s request.
A May 2026 arXiv preprint, “When JSON Is Not Enough: Semantic Reliability of Schema-Constrained LLM Ordering Agents,” reports results from a restaurant-ordering benchmark. Across 2,400 API calls to four open models, the strongest tested model achieved 100% schema validity while semantic success remained near 80%; weaker tested models produced schema-valid unsafe acceptances in double digits. These are findings for that paper’s models, prompts, and benchmark—not a general error rate for LLM APIs.
OpenAI reported 100% schema reliability in its internal evaluations for gpt-4o-2024-08-06. The same 2024 announcement says its model scored 93% on a schema-understanding benchmark before the company added constrained decoding. Those vendor-reported figures concern schema adherence in the stated evaluation, not semantic decision accuracy or results across all models and providers. See OpenAI’s Structured Outputs announcement.
Handle refusals and incomplete responses explicitly
A caller should distinguish a usable decision from a refusal, interrupted output, or application-level validation failure. Do not silently treat an absent or partial response as a default decision. OpenAI’s announcement notes that its schema guarantee applies when the response does not include a refusal and has not been prematurely interrupted, as indicated by finish_reason. Build a separate handling path for these outcomes, such as asking for clarification, retrying where appropriate, or routing the case for review.
Where this pattern is useful
Structured values are useful when downstream software needs to inspect, store, or route a model result. OpenAI’s documentation gives examples including extracting structured records from raw text, fetching data or taking actions through function calls, extracting to-dos and due dates from meeting notes, and generating UI structures from user intent. These are documented use cases, not evidence that any particular workflow will be accurate without testing and validation.
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