Validation Error Array Envelope Prompt Template

Use this prompt when you are building an API endpoint that must return field-level validation errors in a structured, machine-readable format. The primary job-to-be-done is transforming a list of failed validation rules into a consistent error array envelope that downstream clients—such as frontend form handlers, mobile apps, or external integrators—can parse and map directly to user interface elements. The ideal user is a backend engineer or API developer who already has a validation engine producing rule violations and needs the AI to format those violations into a predictable contract without inventing error codes, losing nested field paths, or collapsing multiple errors on the same field.

This prompt is appropriate when your validation logic runs before the AI call and you can supply the raw violation data as structured input. You should provide the list of failed rules, the expected output schema, and any constraints on error codes or message phrasing. Do not use this prompt when the AI itself must decide whether input is valid—that is a classification or rule-evaluation task, not an envelope formatting task. Avoid this prompt for single-error scenarios where a flat error object would suffice; the array envelope adds complexity that is only justified when clients expect multiple field errors per response. Also avoid it when error messages contain dynamic user-facing text that requires localization; handle translation in the application layer, not in the envelope structure.

Before wiring this into production, define your evaluation criteria. Test that the output array contains every input violation without dropping or reordering entries. Verify that nested field paths use consistent notation (e.g., addresses[0].street), that multiple errors on the same field produce separate array entries, and that the error codes match your approved vocabulary. For high-stakes APIs where error messages might expose internal state, add a post-generation review step that strips sensitive details before the response leaves your gateway. Start with the prompt template in the next section, adapt the placeholders to your validation engine's output format, and run it against a golden set of known violations before integrating it into your API pipeline.

This prompt template instructs the model to produce a standard API error envelope containing an array of field-level validation failures. It is designed to be wired directly into an API gateway, backend-for-frontend, or model-serving layer where downstream clients expect a consistent error contract. The template uses square-bracket placeholders for all variable inputs, making it safe to copy, adapt, and parameterize in your application code before each inference call.

textCopy
You are an API response formatter. Your only job is to produce a valid JSON object that follows the exact structure described below. Do not include explanations, markdown fences, or any text outside the JSON object.

## INPUT
- Validation failures: [VALIDATION_FAILURES]
- Request context: [REQUEST_CONTEXT]

## OUTPUT SCHEMA
Return a JSON object with this structure:
{
  "status": 422,
  "error": "Unprocessable Entity",
  "errors": [
    {
      "field": "string (dot-notation path, e.g., 'user.address.city')",
      "code": "string (machine-readable error code, e.g., 'missing_required_field')",
      "message": "string (human-readable description)",
      "constraint": {
        "type": "string (e.g., 'required', 'min_length', 'pattern')",
        "value": "string or number (the constraint that was violated)",
        "provided": "string or null (the value that was provided, if safe to include)"
      }
    }
  ]
}

## CONSTRAINTS
- The 'errors' array must contain one object per field-level failure.
- If a single field has multiple failures, include one entry per failure.
- Use dot-notation for nested field paths (e.g., 'user.address.zip_code').
- Use bracket notation for array indices (e.g., 'items[0].sku').
- The 'code' field must use snake_case machine-readable identifiers.
- The 'message' field must be a complete sentence describing the failure.
- Never include sensitive data in the 'provided' field. Use null if the value is unsafe.
- If no validation failures exist, return an empty 'errors' array with status 200.

## EXAMPLES
Input: Missing 'email' field, invalid 'age' value
Output:
{
  "status": 422,
  "error": "Unprocessable Entity",
  "errors": [
    {
      "field": "email",
      "code": "missing_required_field",
      "message": "The 'email' field is required.",
      "constraint": { "type": "required", "value": "present", "provided": null }
    },
    {
      "field": "age",
      "code": "invalid_type",
      "message": "The 'age' field must be an integer.",
      "constraint": { "type": "type", "value": "integer", "provided": "twenty-five" }
    }
  ]
}

## RISK_LEVEL
[RISK_LEVEL]

Generate the JSON output now.

To adapt this template, replace each square-bracket placeholder with the appropriate data or instruction for your use case. [VALIDATION_FAILURES] should contain the list of field-level failures, typically sourced from a schema validator, type checker, or business logic layer. [REQUEST_CONTEXT] can include the original request payload, endpoint path, or user identifier to help the model construct accurate field paths. [RISK_LEVEL] should be set to 'low', 'medium', or 'high' to control whether the model includes the provided value in constraint details—set to 'high' to force provided to null for all entries. Before deploying, run this prompt against your eval suite to verify nested path notation, array index formatting, and multiple-error-per-field scenarios produce valid, parseable JSON that matches your API contract.

The Validation Error Array Envelope prompt is designed to sit behind an API endpoint as a post-processing or direct-generation step. Its primary job is to accept a list of raw validation failures—typically produced by a schema validator, ORM, or business logic layer—and format them into a consistent, client-consumable error array. This harness is not a replacement for your actual validation logic; it is a formatting layer that ensures every error object contains the required field, code, message, and optional constraints fields, using consistent array index notation for nested paths.

To wire this into an application, place the prompt after your primary validation engine runs. Collect the raw failures into a structured input object containing the resource_type, an errors array of raw failure tuples, and any [CONSTRAINTS] like locale or max errors per field. The model call should be wrapped in a thin service function that enforces a strict JSON Schema on the response. If the model output fails schema validation, implement a single retry by feeding the validation error back into the prompt's [PREVIOUS_OUTPUT] and [VALIDATION_ERRORS] placeholders. After one retry, if the output still fails, log the raw model response and fall back to a hardcoded, minimal error envelope to avoid blocking the client. This ensures the API never returns a malformed error body to the consumer.

For production deployment, choose a fast, low-cost model for this formatting task since the logic is structural rather than creative. Log every model call with the input failure count, output error count, and schema validation pass/fail status. Set up an alert if the retry rate exceeds 2% or if the fallback path is triggered, as this indicates prompt drift or a model behavior change. Crucially, never pass unsanitized internal stack traces or database error messages into the prompt's input; the raw failures should already be scrubbed to contain only client-safe field paths and constraint descriptions. The prompt's output should be treated as the final error response body, so human review is not required per request, but the prompt version and eval results should be reviewed before any deployment to production.