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An LLM decision API returns a typed object—such as a category, amount, or action proposal—that an application can read directly, instead of asking software to interpret a paragraph. This can reduce formatting failures, but it does not make a decision correct: applications still need to validate the values, apply business rules, and authorize consequential actions.
What “values, not text” means
In this architectural pattern, the model’s response has named fields and defined types that the application can consume: for example, an intent label, a requested date, and a confidence or explanation field. The application handles those values as data instead of extracting them from prose.
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“LLM decision API” is a descriptive pattern, not a universal product or standard established by the available documentation. In OpenAI’s terminology, a structured response format shapes the model’s reply; function calling connects the model to application functions, tools, or data. See Structured model outputs and Function calling.
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Choose the right output mechanism
| Option | What it is for | What it does not establish |
|---|---|---|
| JSON mode | Producing JSON that can be parsed. | It does not ensure that the object conforms to a particular schema. OpenAI makes this distinction in its Help Center documentation. |
| Structured Outputs | Constraining a response to a supplied, supported schema. | Schema adherence does not prove that the values are factually or semantically right. Check model compatibility and supported schema features in the current guide. |
| Function calling | Having the model select or invoke application functionality, such as fetching data or taking an action. | A function call is not simply a structured answer for the caller; the application must decide whether and how to execute it. Strict function calling also has schema requirements, including required fields and additionalProperties set to false. See the function-calling guide. |
OpenAI describes structured outputs as useful for extracting structured records from raw text and for shaping responses, while function calling is intended for connecting models with functions and data. Its examples include extracting to-dos, due dates, and assignments from meeting notes. These are documented use cases, not independent evidence that every such workflow will be accurate.
Design the contract before writing the prompt
Specify the object your application expects before relying on model output. Define field names and types, required values, allowed enum members, and what to return when information is missing or ambiguous. A schema should make invalid shapes harder to pass downstream, while explicit ambiguity handling prevents the model from being forced to invent a value.
- Distinguish fields that are required from fields that may legitimately be absent or unknown.
- Use bounded values, such as enums, where the application has a known set of choices.
- Define how refusals and incomplete responses are represented and handled.
- Confirm the selected model and endpoint support the schema features you depend on; incompatible schemas or unsupported features can affect behavior.
JSON that parses is not necessarily JSON that matches your intended contract. OpenAI’s Help Center states that JSON mode guarantees parseable JSON, not conformance to a specific schema. Structured Outputs are designed for the latter, within supported models and schema features.
Schema validity is not decision correctness
A response can have every required field and still misunderstand the request, select an unsafe option, or violate a business rule. OpenAI reported 100% schema reliability in internal evaluations for gpt-4o-2024-08-06; that is a vendor-reported result about schema matching in that setup, not a measure of semantic decision accuracy or a guarantee for other models. OpenAI also reported 93% on its schema-understanding benchmark before describing its constrained-output approach. Those figures come from the vendor’s account and should not be read as directly comparable independent benchmarks or general product success rates. See OpenAI’s Structured Outputs announcement.
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Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →A 2026 preprint, “When JSON Is Not Enough: Semantic Reliability of Schema-Constrained LLM Ordering Agents”, reports 2,400 API calls across four open models in a restaurant-ordering benchmark. 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 the paper’s models, prompts, and ordering benchmark—not a universal error rate for decision APIs.
Before a returned value triggers a purchase, booking, account change, or other consequential action, validate it against application rules and authorization requirements. Treat model output as a proposal, not permission to execute.
Handle failures as explicit outcomes
Do not assume every response contains a usable decision object. OpenAI’s Structured Outputs announcement qualifies its schema-matching guarantee: the response must not include a refusal and must not have been prematurely interrupted, as indicated by finish_reason. A refusal, interrupted response, or application validation failure needs its own handling path rather than being silently treated as a decision.
- Refusal: detect and route it as a refusal, not as an ordinary object with a missing field.
- Interruption or truncation: check completion status before parsing or acting on the response.
- Schema or parsing failure: reject the result and follow a safe fallback or retry policy.
- Semantic or business-rule failure: block execution, request clarification, or route for review according to the risk of the action.
These checks separate three distinct questions: can the application parse the response, does it match the expected structure, and is the proposed decision safe and correct enough to use?
Where this pattern fits
Typed model outputs are useful when an application needs data extracted from unstructured input or a bounded proposal that can be checked before use. OpenAI’s documented examples include data extraction, fetching information, computation, taking actions through functions, and generating UI structures from user intent. Those examples describe supported patterns, not a guarantee of accuracy for a particular deployment.
Use a structured response when the caller needs data back. Use function calling when the model should connect to application capabilities. In either case, keep validation and execution policy in the application: constrained output can control shape, but it cannot replace the rules that determine what the system is allowed to do.
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