Structured Output Contract Verification Prompt Template

This prompt is designed for API and platform teams who need to verify that AI-generated structured outputs conform to a strict contract before those outputs reach a downstream parser, database, or client. Use it when a malformed JSON object, a missing required field, or a type mismatch would break an integration. It acts as a programmatic gate, not a human review tool. The prompt expects a target schema and a candidate output, and it returns a detailed, machine-readable compliance report. Do not use this prompt for evaluating the semantic quality of the content inside the fields; it only verifies structural and type-level adherence.

The ideal user is an engineering lead or developer integrating an LLM into a production pipeline where the output must be parsed by another service. The required context includes a formal schema definition (JSON Schema, OpenAPI, or a strict type specification) and the raw candidate output string. This prompt is not a substitute for a JSON linter or a static type checker; it is a semantic validation layer that can reason about schema intent, handle near-miss formatting errors, and produce a structured compliance report that your application can act on. Do not use this prompt for free-text quality evaluation, content moderation, or any task where the primary concern is the meaning of the text rather than its structure.

Before wiring this prompt into a production system, define your failure tolerance. A single missing optional field may be acceptable, while a missing required field or a type mismatch should halt the pipeline. The prompt's output schema is designed to be parsed by your application, so you can implement conditional logic: pass the output through on a clean report, route to a repair prompt on minor issues, or escalate for human review on critical failures. Always log the full compliance report alongside the candidate output for debugging and audit trails.

This prompt template is the core of the Structured Output Contract Verification workflow. It instructs the model to act as a schema validator, comparing a generated output against a target schema and producing a structured compliance report. The template uses square-bracket placeholders for all variable inputs, making it safe to paste directly into an evaluation harness, a CI/CD pipeline, or a manual testing tool. Replace each placeholder with the actual values for your specific test case before execution.

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You are a strict schema compliance auditor. Your task is to verify whether the provided [OUTPUT_PAYLOAD] conforms to the [TARGET_SCHEMA].

Perform the following checks:
1. Parse the [OUTPUT_PAYLOAD] according to its declared format ([OUTPUT_FORMAT], e.g., JSON, XML, YAML).
2. Validate the parsed structure against the [TARGET_SCHEMA].
3. For every field defined as required in the [TARGET_SCHEMA], verify its presence in the [OUTPUT_PAYLOAD].
4. For every field present, verify its data type matches the type defined in the [TARGET_SCHEMA].
5. For any field with enumerated values (enums) defined in the [TARGET_SCHEMA], verify the value is one of the allowed options.
6. Check for any additional constraints specified in [EXTRA_CONSTRAINTS].

Report your findings in the following JSON structure only. Do not include any text outside the JSON object.

{
  "overall_compliance": boolean,
  "schema_parseable": boolean,
  "output_parseable": boolean,
  "errors": [
    {
      "field_path": "string (e.g., 'user.address.city')",
      "error_type": "MISSING_REQUIRED | TYPE_MISMATCH | INVALID_ENUM | CONSTRAINT_VIOLATION | UNKNOWN_FIELD",
      "expected": "string",
      "actual": "string",
      "message": "string"
    }
  ],
  "warnings": [
    {
      "field_path": "string",
      "message": "string"
    }
  ]
}

[OUTPUT_PAYLOAD]:
[PAYLOAD]

[TARGET_SCHEMA]:
[SCHEMA]

[OUTPUT_FORMAT]: [FORMAT]

[EXTRA_CONSTRAINTS]: [CONSTRAINTS]

To adapt this template, replace the five primary placeholders: [PAYLOAD] with the raw output string to be verified, [SCHEMA] with the target schema definition (e.g., a JSON Schema object, an XML Schema Definition, or a plain-text description of required fields and types), [FORMAT] with the expected format like JSON or XML, and [CONSTRAINTS] with any additional rules such as 'no additional properties allowed' or 'string fields must not be empty'. The [OUTPUT_PAYLOAD] and [TARGET_SCHEMA] labels in the instructions are fixed; only the values after the colons should be substituted. For high-risk domains like finance or healthcare, always route the resulting error report for human review before accepting the output as valid. Wire this prompt into an automated harness that parses the JSON response and gates downstream processing on the overall_compliance boolean.

This prompt is designed to be called programmatically after a primary generation step. The typical flow is: (1) generate structured output from a model, (2) parse the output, (3) if parsing fails or you want a deeper semantic check, call this verification prompt with the raw output and the target schema, (4) act on the field-level pass/fail results. Do not use this prompt as a real-time guard in a synchronous user-facing loop where latency is critical—it adds a second model call. Instead, run it asynchronously in a validation worker, a CI/CD step, or a post-generation audit queue. For high-throughput systems, batch multiple outputs into a single verification call by providing an array of [OUTPUTS] and adjusting the [OUTPUT_SCHEMA] to expect a list of verification reports.

Validation and retry logic: Parse the verification prompt's JSON response and check the top-level overall_compliant boolean. If false, iterate through the field_results array to collect all FAIL and MISMATCH entries. Use these structured diagnostics to drive a repair loop: feed the original output, the failure details, and the schema back to the generation model with a repair instruction. Limit retries to 2-3 attempts to avoid infinite loops. Model choice: Use a model with strong JSON output and instruction-following capabilities (e.g., gpt-4o, claude-3-5-sonnet). Avoid smaller or older models that may hallucinate field names or produce malformed verification reports. Logging: Log every verification call with prompt_version, model, schema_hash, output_hash, overall_compliant, and the full field_results array. This audit trail is essential for debugging schema drift and model behavior changes over time. Tool use: If your platform supports structured output modes (e.g., OpenAI's response_format with a JSON Schema, or Anthropic's tool use with a defined verification_report schema), use them to guarantee the verification prompt's output shape. This eliminates a class of parsing errors where the verifier itself produces malformed JSON.

CI/CD integration: In a pre-release pipeline, run this verification prompt against a golden dataset of known outputs and schemas. Assert that overall_compliant matches expected values and that no regressions appear in field_results. Fail the build if the verification pass rate drops below a configured threshold (e.g., 99%). Human review trigger: For high-risk domains (finance, healthcare, legal), configure an escalation rule: if overall_compliant is false and any field_result.severity is CRITICAL, route the output to a human review queue instead of attempting automated repair. The verification report's field_results array provides the exact context a reviewer needs to decide. What to avoid: Do not skip schema normalization—ensure both the target schema and the generated output are canonicalized (e.g., sorted keys, consistent indentation) before passing them to the verifier. Do not treat a PASS from this prompt as a guarantee of semantic correctness; it verifies structural and type compliance, not business logic accuracy. Pair this prompt with a separate groundedness or factual accuracy evaluation when the content of fields matters as much as their shape.