Output Contract to Schema Enforcement Example Prompt

This prompt is designed for the moment when you have a formal output contract—a JSON Schema, an OpenAPI response object, a TypeScript interface, or a written specification—and you need the model to internalize the expected structure through demonstration rather than procedural instruction. Instead of writing verbose field-by-field rules that consume hundreds of tokens and still produce drift, you provide the raw contract and this prompt generates input-output demonstration pairs. These pairs teach the model the expected structure, enum values, null handling rules, and field presence requirements by example. This approach is particularly effective for nested objects, conditional fields, and enum-constrained values where instruction-only prompts often produce inconsistent results.

Use this prompt when the output contract is complex enough that procedural instructions become brittle. Look for contracts with more than five fields, nested objects, arrays of typed objects, conditional field presence (e.g., discount only appears when type is sale), or enum-constrained values. The generated examples should be validated against the original contract using a schema validator before they are included in your production prompt. A good practice is to generate three to five example pairs, run them through a validation harness that checks field presence, type correctness, and enum membership, and then select the two or three that produce the highest validation pass rate when used as few-shot demonstrations. If your contract includes nullable fields, ensure the generated examples include both populated and null cases so the model learns the distinction between a missing field and an empty value.

Do not use this prompt when the output contract is trivial—a flat object with fewer than five fields rarely justifies the example generation overhead. Avoid it when you need real-time schema validation rather than format teaching; this prompt shapes model behavior, it does not replace a post-processing validator. Also avoid it when the contract changes frequently and example maintenance overhead exceeds the token savings. In those cases, a structured output API with strict JSON mode and a server-side validator is more reliable than maintaining few-shot examples. If you proceed, always pair the generated examples with a validation step in your application harness—never trust that the examples alone will prevent all schema violations in production.

This prompt template transforms a formal output contract—such as a JSON Schema, OpenAPI response definition, or typed interface specification—into a set of few-shot examples that teach a model to produce strictly compliant outputs. Instead of relying on verbose field-by-field instructions that consume hundreds of tokens and still permit schema drift, the prompt generates compact demonstration pairs that show the model exactly what valid output looks like, including edge cases like null fields, empty arrays, and enum-constrained values. The generated examples can be inserted directly into system prompts, function-calling definitions, or structured output instructions for production inference.

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You are an output contract to few-shot example converter. Your task is to convert a formal output schema into a set of demonstration examples that teach a model to produce strictly compliant structured output.

## INPUT

Output Contract Specification:
[OUTPUT_CONTRACT]

Target Number of Examples: [EXAMPLE_COUNT]

Example Diversity Requirements:
[DIVERSITY_REQUIREMENTS]

## INSTRUCTIONS

1. Parse the provided output contract to identify:
   - All required fields and their types
   - All optional fields and their default or null behavior
   - All enum-constrained fields and their valid values
   - Nested object structures and array element types
   - Field presence rules (when a field must appear vs. when it can be omitted)

2. Generate [EXAMPLE_COUNT] input-output demonstration pairs where:
   - Each output strictly conforms to the contract
   - Examples collectively cover: normal cases, edge cases (nulls, empties, maximum nesting), and at least one example where every optional field is populated
   - Enum fields cycle through all valid values across the example set
   - Array fields include examples of empty arrays, single-element arrays, and multi-element arrays
   - Nested objects appear in at least two examples with different internal states

3. For each example, include a brief comment explaining which contract requirement it demonstrates.

4. Format each demonstration pair as:

Input: [representative input that would trigger this output] Output: [strictly compliant JSON output] Demonstrates: [which contract rules this example covers]

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## CONSTRAINTS

- Do not invent fields that are not in the contract
- Do not omit required fields in any example
- Use null (not the string "null") for absent optional fields unless the contract specifies a different default
- Preserve exact enum values, including casing
- Output valid JSON that would pass validation against the original schema
- If the contract specifies date formats, use ISO 8601

## OUTPUT FORMAT

Return a JSON object with:
- "contract_summary": brief description of the contract structure
- "examples": array of demonstration objects, each containing "input", "output", and "demonstrates" fields
- "coverage_notes": which contract requirements are covered and which (if any) need additional examples

To adapt this prompt for production use, replace the square-bracket placeholders with concrete values. The [OUTPUT_CONTRACT] placeholder should receive the full schema definition—whether JSON Schema, OpenAPI response object, TypeScript interface, or a plain-English field specification. Set [EXAMPLE_COUNT] to a number between 3 and 10; fewer than 3 examples rarely cover edge cases adequately, while more than 10 often duplicates patterns without adding coverage. The [DIVERSITY_REQUIREMENTS] placeholder lets you specify domain-specific coverage needs, such as "include examples with non-English text in string fields" or "ensure at least one example has all numeric fields at zero." After generating the examples, validate each output against the original contract using a JSON Schema validator or equivalent tool before inserting the examples into your production prompt. For high-stakes integrations where schema compliance is critical, run the generated examples through an automated eval that checks field presence, type correctness, enum validity, and null handling against the contract specification.

The Output Contract to Schema Enforcement prompt is designed to sit inside a validation loop where the model's output is checked against the original contract specification before being accepted. This prompt teaches the model to produce strictly typed, schema-conformant examples, but it does not guarantee compliance on every call. The implementation harness must therefore treat the model's output as a candidate that requires verification, not a trusted result. The core integration pattern is: generate candidate examples, validate them against the contract schema, retry with error feedback if validation fails, and escalate to human review if retries are exhausted or the contract is high-risk.

Start by defining a strict validation function that checks every field in the generated example against the original contract. This validator should verify: (1) all required fields are present, (2) field types match the contract specification exactly, (3) enum values are restricted to allowed sets, (4) null values appear only where the contract permits them, and (5) nested objects and arrays conform to their subschemas. When validation fails, capture the specific error messages—such as missing required field 'customer_id' or enum violation: 'status' must be one of ['active', 'inactive', 'pending']—and feed them back into the prompt's [CONSTRAINTS] or [ERROR_FEEDBACK] placeholder on the next retry. Implement a retry loop with a maximum of 3 attempts. On each retry, append the previous invalid output and the validator's error messages to the prompt context so the model can see what went wrong. Log every attempt, including the raw model output, validation result, and retry count, to a structured logging system for debugging and audit purposes.

For high-risk contracts—such as those governing financial transactions, healthcare data, or legal documents—add a human review gate after the retry loop. If the model fails validation after all retries, route the failed output and error context to a review queue rather than silently falling back to a default or truncated payload. The review interface should display the original contract specification, the model's best attempt, and the specific validation failures. For lower-risk contracts, you may implement a graceful degradation path: if retries are exhausted, return a partial output with missing fields explicitly set to null and attach a validation_warnings array documenting what couldn't be resolved. Choose a model with strong JSON mode or structured output support (such as GPT-4 with response_format set to json_object or Claude with explicit schema instructions) to reduce the base failure rate. Avoid using models without native structured output guarantees for this workflow, as the retry rate will be prohibitively high.

Before deploying, build a regression test suite from your contract specification. Generate a golden set of inputs that exercise every field, every enum value, every nullable field, and every nested structure in the contract. Run the prompt against these inputs and confirm that the validator accepts all outputs. Store these test cases and re-run them whenever the prompt, the contract, or the model version changes. If you're using this prompt to generate examples that will themselves be used in other prompts (as few-shot demonstrations), add a second validation layer that confirms the generated examples produce correct downstream model behavior. A generated example that passes schema validation but teaches the wrong pattern is a silent failure that will propagate through your system.