Typed Object Generation Prompt for API Payloads

This prompt is for backend developers and platform engineers who need an LLM to generate API request bodies that can be inserted directly into a service without post-processing. It targets workflows where a downstream API contract is non-negotiable: every field must match its declared type, nullability must be explicit, and numeric coercion must be prevented. Use this when you are building AI-assisted data entry, migration scripts, or any pipeline where the model output feeds directly into an HTTP client or database driver. The core job-to-be-done is eliminating the manual or programmatic repair step between model generation and API ingestion.

Do not use this prompt for free-form JSON generation, conversational responses, or outputs that will be reviewed and manually corrected before ingestion. It is also the wrong choice when the target schema is ambiguous, when field types can vary based on polymorphic dispatch, or when the downstream system tolerates loose typing. If your pipeline already includes a robust validation and repair layer that can fix type coercion issues, a simpler prompt may suffice. This prompt is specifically designed for high-cadence, automated pipelines where a single type error causes a hard failure in the next system.

Before adopting this prompt, confirm that you have a stable, versioned API contract to provide as the [OUTPUT_SCHEMA]. The prompt works best when paired with a validation harness that checks every field against the schema immediately after generation, logs type violations, and triggers a retry or escalation path. If your use case involves regulated data, financial records, or healthcare information, add a human review step before the payload reaches the target system. Start by copying the prompt template, replacing the placeholders with your concrete schema and constraints, and then run it against a golden test set to measure field-level accuracy before integrating it into your application.

The following prompt template is designed to be dropped into your application's generation layer. It instructs the model to produce a single JSON object that conforms to a target schema you provide. The template uses square-bracket placeholders for all dynamic parts—schema, field descriptions, source data, and rules—so you can populate them programmatically before each call. This keeps the prompt deterministic and auditable.

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You are a strict JSON generator. Your only job is to produce a single JSON object that exactly matches the provided schema, field descriptions, nullability rules, default values, and coercion prevention rules. Do not add commentary, explanations, markdown fences, or extra text.

TARGET_SCHEMA:
[TARGET_SCHEMA]

FIELD_DESCRIPTIONS:
[FIELD_DESCRIPTIONS]

SOURCE_DATA:
[SOURCE_DATA]

NULLABILITY_RULES:
- Fields marked as nullable may be null only when the source data provides no value and no default is specified.
- Fields marked as non-nullable must never be null. If source data is missing, use the specified default value. If no default is specified, omit the field only if the schema permits it; otherwise, raise a clear error in a structured error field.
[NULLABILITY_RULES]

DEFAULT_VALUES:
[DEFAULT_VALUES]

COERCION_PREVENTION_RULES:
- Never convert a string to a number or a number to a string.
- Never convert an integer to a float or a float to an integer.
- Never convert a boolean to a string or number.
- Never convert null to an empty string, zero, or false.
- If the source data type does not match the target field type, do not coerce. Instead, omit the field if optional, or populate an errors array with a structured error object containing field name, expected type, received type, and received value.
[COERCION_PREVENTION_RULES]

OUTPUT_SCHEMA:
{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "type": "object",
  "properties": {
    "data": {
      "type": "object",
      "description": "The generated payload matching TARGET_SCHEMA. Null if generation failed."
    },
    "errors": {
      "type": "array",
      "description": "Structured error objects when fields cannot be populated correctly.",
      "items": {
        "type": "object",
        "properties": {
          "field": { "type": "string" },
          "expected_type": { "type": "string" },
          "received_type": { "type": "string" },
          "received_value": {},
          "rule_violated": { "type": "string" }
        },
        "required": ["field", "expected_type", "received_type", "rule_violated"]
      }
    }
  },
  "required": ["data"]
}

Return only the JSON object matching OUTPUT_SCHEMA above. No markdown fences, no commentary.

To adapt this template, replace each bracketed placeholder with your actual constraints. [TARGET_SCHEMA] should contain your desired output schema as a JSON Schema object. [FIELD_DESCRIPTIONS] should include natural-language descriptions of each field's purpose and expected content. [SOURCE_DATA] is the unstructured or semi-structured input the model should transform. [NULLABILITY_RULES] and [DEFAULT_VALUES] let you specify per-field null behavior and fallback values. [COERCION_PREVENTION_RULES] can be extended with domain-specific rules, such as date format preservation or enum value locking.

Before shipping this prompt, wire the output through a JSON Schema validator using the TARGET_SCHEMA you provided. If the errors array is non-empty, log the structured error objects and decide whether to retry, fall back to a default payload, or escalate for human review. For high-risk domains—such as finance, healthcare, or legal—require human approval when any field fails validation. This template works best with models that support structured output or JSON mode; if your model does not, add an explicit instruction to wrap the JSON in a fenced code block and strip the fences in post-processing.

Before calling the model, assemble the prompt by injecting your API schema, field descriptions, source data, and rules into the placeholders. Treat the schema and constraint blocks as a reusable prefix that can be cached across requests when the target payload shape remains stable. For high-throughput pipelines, precompute the schema portion of the prompt once and append only the variable [INPUT_DATA] and [CONTEXT] blocks per request. This reduces token processing overhead and improves latency consistency.

After receiving the model response, strip any markdown fences and parse the JSON. Run the parsed object through a JSON Schema validator configured with your target schema. If validation fails, log the validation errors with the request ID and either retry with the errors injected back into the prompt as [PREVIOUS_ERRORS] or route to a dedicated repair prompt. Set a maximum of two retry attempts before escalating to a human review queue or returning a structured error to the caller. For sensitive payloads, ensure source data is redacted before it enters the prompt and that generated payloads are logged only after PII scrubbing.

Configure a timeout and max token limit appropriate for your payload size. A 4,000-token output limit is sufficient for most single-record API payloads; increase it only when generating bulk record arrays. Use structured logging to capture schema version, validation pass/fail status, retry count, and latency per generation call. This telemetry is essential for detecting schema drift, model behavior changes after provider updates, and regressions in field-level type accuracy. Always version your prompt template alongside your API schema so that generation behavior is traceable to a specific prompt-schema pair.