An AI API is often introduced with an endpoint and a successful example response. That is not enough to tell a consumer what the result means. A system can accept a request, return valid JSON, and still leave the underlying question unresolved. Designing the contract first helps a team distinguish a technical exchange from a reviewed judgment before implementation makes ambiguous assumptions harder to change.
This guide develops a paper specification for an imaginary document-classification service. It is a design exercise, not documentation for an operating NeuroBrowser.com API. There are no live endpoints, account keys, or hosted inference services on this site. The goal is to define an interface that another developer and an editorial reviewer could discuss without needing a running system.
Begin with the decision the interface supports
Write the consumer’s task in ordinary language. In the example, a research application sends an approved document excerpt and receives a proposed reading-queue label with supporting evidence. The application does not receive a declaration that the document is scientifically valid. That boundary belongs in the contract because it determines what downstream software may reasonably assume.
Describe the responsible reviewer and what happens after a result arrives. A proposed label might enter a queue, be displayed beside the source, or remain unresolved. Do not let the convenience of an API response silently remove the human decision that the original workflow required. The interface should preserve the task’s meaning rather than merely move text between components.
Define the input before inventing field names
List the information the task actually needs: the document identifier, supplied text, label-handbook version, and permitted processing context. Decide which fields are required, optional, or deliberately excluded. Do not include personal information or large source attachments by default when an approved excerpt would satisfy the task. Keep the minimum necessary input visible during design review.
Describe size and format boundaries as explicit constraints to be chosen for the implementation. Avoid publishing arbitrary limits as though they were already measured. State what happens to an unsupported format or incomplete request. A consumer should be able to distinguish a rejected input from a request that was processed but did not produce a usable classification.
Give output states understandable meanings
Separate technical failure, valid-but-unresolved output, and a proposed classification awaiting review. Those states represent different events and require different next steps. In the paper example, an unresolved result should explain whether evidence was missing, labels overlapped, or the item fell outside the intended collection. It should not be disguised as an empty successful label.
Write an example for every state, not just the attractive one. Ask a consumer developer to explain what their application would display and what a reviewer would do next. If two states lead to confusion, refine the definitions before implementing them. Consistent field names cannot repair a contract whose meanings remain ambiguous.
Specify evidence as part of the response
Include a reference to the supplied material and the passage supporting the proposed label. Define how that reference remains connected to the version of the input that was processed. A consumer should not need to trust an isolated model paraphrase in order to review the decision. Preserve the original source and make the proposed interpretation visibly separate from it.
Evidence is not a decorative explanation field
Decide how the consumer checks that a cited passage is present in the input and relevant to the selected label. A field called “reason” may contain fluent prose without usable evidence. The LLM classification guide explains why a label handbook and source review remain necessary even when a response follows the expected structure.
Validate shape without confusing it with truth
A schema can express expectations about objects, properties, required fields, and allowed values. Use it to make malformed or unsupported responses recognizable. Decide whether unexpected fields are permitted and how a missing value differs from an explicit null. Keep these decisions consistent across successful, unresolved, and failed responses rather than letting each example imply a different rule.
Then define the checks that a schema cannot settle by itself. Does the label follow the handbook? Does the cited passage support it? Is the input appropriate for the intended task? Treat structural validation and substantive review as separate stages. A valid object is evidence that a formatting contract was satisfied, not that the answer is correct or suitable for use.
Version the contract and the model separately
The interface shape, label definitions, prompt, and model configuration can change independently. Give each the identifier needed to interpret an output later. Do not rely on one generic version string to explain every kind of change. A consumer may need to know that the response shape is stable while the underlying label handbook has been revised.
Write a compatibility policy before changing fields. Decide which changes are additive, which require a new contract version, and how older consumers should behave. Keep examples of earlier outputs available during review. A contract becomes easier to maintain when a developer can identify exactly which promise changed instead of discovering it through an unexpected production response.
Plan retry behavior and duplicate work
Describe what happens when a consumer loses the connection after submitting a request. Can it safely try again, and how will repeated submissions be identified? A document-classification task may not change external records directly, but duplicate processing can still create confusing review entries or unnecessary work. Define the intended behavior rather than leaving it to the consumer’s guess.
Keep request identity separate from the source document identifier when the same document can be evaluated under different conditions. Record the relevant version and task context. Avoid promising identical output across repeated model calls unless that behavior has been established for the actual implementation. The paper contract should state the assumptions that testing will need to verify.
Put credentials and data boundaries outside public examples
Never design a workflow that requires embedding private service credentials in publicly delivered browser code. Explain which component would hold authority to call the service and how that component is approved. For a static educational website, the correct choice may be no live integration at all. A concept page can teach the contract without pretending to run an endpoint.
Record the handling of inputs, outputs, logs, and retained review decisions. Do not claim privacy properties simply because a request uses a familiar format. Data movement and storage are architectural questions that require their own review. The AI API topic keeps those boundaries explicit, while the LLM evaluation page covers the quality checks around the proposed answer.
Test the contract on paper with difficult cases
Prepare an ordinary example, an unsupported input, a missing field, a contradictory source, and a technically unavailable service. Ask a consumer developer and a reviewer to walk through each case. Record what the interface shows, what it preserves, and whether it could trigger an inappropriate automatic action. This exercise can reveal ambiguity before code creates an appearance of completeness.
Include a case in which the source changes after processing. Can the consumer still identify the version behind the result? Include another in which the label handbook changes while the document remains the same. A useful contract makes those differences explainable instead of treating every changed answer as an unexplained model failure.
Conclusion: specify uncertainty as carefully as success
A clear AI interface tells consumers what was accepted, what was produced, what evidence supports it, and what remains unresolved. Build those meanings before naming endpoints or designing a demonstration button. Separate schema validity from judgment, version the relevant components, and rehearse failure states. A reviewable paper contract is a stronger starting point than a live example with undocumented assumptions.
The supporting technical reference is the JSON Schema object documentation, which explains object properties, required fields, and related structural rules. The proposed service and workflow above are original design examples. They are not a deployed API, a security guarantee, or a claim that structural validation establishes the truth of model output.