Structured Output Extraction AI Prompt

Design prompts that reliably extract structured data from LLM outputs.

Input type: {{input_type}} (free text, documents, conversations, web content)
Required output schema: {{schema}}
Model: {{model}}
Failure tolerance: {{failure_tolerance}} (best effort vs guaranteed schema compliance)

1. JSON output prompting:
   Explicit schema specification:
   'Extract the following information from the text and return ONLY a valid JSON object with no additional text, markdown formatting, or code blocks.

   Required fields:
   - name (string): full name of the person
   - date (string, ISO 8601 format YYYY-MM-DD or null if not found)
   - amount (number or null): monetary amount in USD
   - sentiment (string, one of: "positive", "neutral", "negative")

   If a field is not found in the text, return null for that field.
   Do not invent information not present in the text.

   Text to extract from:
   {{text}}'

2. Enforcing schema compliance:

   OpenAI Structured Outputs:
   - Provide a JSON schema in the API request; the model is constrained to produce valid output
   - response_format={"type": "json_schema", "json_schema": {"name": "...", "schema": {...}}}
   - Requires: careful schema design (all required fields specified, correct types)

   Instructor library (Python):
   - Define a Pydantic model as the expected output
   - Instructor wraps the LLM call and retries if the output fails Pydantic validation
   - Handles retries automatically (typically 1-3 retries resolves most failures)

   Outlines / Guidance:
   - Force the model to follow a grammar or regex pattern at the token level
   - Guaranteed valid output; some quality tradeoff for very constrained grammars

3. Extraction failure handling:
   - Parse the output; if parsing fails: retry with additional instructions
   - Retry prompt addition: 'Your previous response could not be parsed as JSON. Please return only valid JSON with no other text.'
   - After 3 retries: log as extraction failure and route for manual review

4. Nested and array schemas:
   - For arrays: 'Return a JSON array of objects, each with fields: ...'
   - For nested objects: define the nested schema explicitly
   - Limit nesting depth to 3 levels for reliable extraction

5. Hallucination prevention for extraction:
   - Always add: 'Only extract information explicitly stated in the text'
   - For optional fields: 'If the field is not clearly mentioned, return null — do not infer or guess'
   - Post-extraction validation: verify extracted values are actually present in the source text

Return: extraction prompt template, schema specification, compliance enforcement approach, retry logic, and hallucination prevention rules.