Schema Enforcement Prompt AI Prompt

Design a prompt pattern that enforces strict output schema adherence even when the input data is ambiguous or incomplete.

The challenge: when input data is messy, LLMs tend to improvise — inventing field names, changing types, or nesting structures differently than specified. Schema enforcement prevents this.

1. Hard schema specification:
   - Include the schema as a JSON Schema definition, not just a description
   - Specify for each field: type, required/optional, allowed values, format constraints
   - Example:
   ```json
   {
     "type": "object",
     "required": ["entity_id", "entity_type", "confidence"],
     "properties": {
       "entity_id": {"type": "string"},
       "entity_type": {"type": "string", "enum": ["person", "organization", "location"]},
       "confidence": {"type": "number", "minimum": 0, "maximum": 1}
     },
     "additionalProperties": false
   }
   ```
   - The `additionalProperties: false` is critical — prevents the model from adding extra fields

2. Ambiguity resolution rules (included in the prompt):
   - 'If a value cannot be determined: set required fields to null, omit optional fields entirely'
   - 'Never invent a value for a field. If the value is not in the input, it is null or omitted.'
   - 'If a value does not match the allowed enum values, map it to the closest matching enum value. If no mapping is appropriate, set to null.'

3. Type coercion instructions:
   - 'Numbers that appear as strings must be converted to numeric type: "42" → 42'
   - 'Boolean values may appear as: yes/no, true/false, 1/0 — normalize to boolean'
   - 'Dates must be converted to ISO 8601 format regardless of input format'

4. Client-side schema validation as the final safety net:
   ```python
   from jsonschema import validate, ValidationError
   def validate_output(output, schema):
       try:
           validate(instance=output, schema=schema)
           return output
       except ValidationError as e:
           # Re-prompt with the validation error
           return retry_with_correction(output, e.message)
   ```

5. Schema versioning:
   - Include a schema_version field in the prompt and in the expected output
   - When the schema changes, increment the version — this prevents old cached responses from being used

Return: schema enforcement prompt template, JSON Schema definition pattern, client validation code, retry-on-failure logic, and test cases for ambiguous inputs.