Structured Data Extraction Prompt AI Prompt

Write a prompt that reliably extracts structured data from unstructured text.

Source text type: {{text_type}} (e.g. customer support tickets, invoice PDFs, clinical notes, news articles)
Target schema: {{target_schema}} (the fields you want to extract)

Apply these prompt engineering principles for data extraction:

1. Schema-first instruction:
   - Define the output schema explicitly before showing any examples
   - Name every field, its type, and what to do when it is missing (null vs omit vs default value)
   - Example: 'Extract the following fields. If a field is not present in the text, return null for that field.'

2. Constraint specification:
   - State the output format unambiguously: 'Return ONLY a JSON object. No explanation, no markdown, no preamble.'
   - Specify value formats: 'Dates must be in ISO 8601 format (YYYY-MM-DD)', 'Monetary values as numbers without currency symbols'
   - Specify enumeration constraints: 'status must be one of: [open, closed, pending]'

3. Ambiguity resolution rules:
   - What should the model do when a field is ambiguous? Provide explicit tie-breaking rules.
   - Example: 'If multiple dates appear, extract the most recent one as order_date'
   - Example: 'If the customer name appears in multiple formats, use the version that includes both first and last name'

4. Negative examples:
   - Show what NOT to include: 'Do not extract dates from headers or footers'
   - Show what NOT to infer: 'Do not infer fields that are not explicitly stated in the text'

5. Robustness to messy input:
   - Instruct the model to handle OCR errors, typos, and inconsistent formatting gracefully
   - 'If a field contains obvious OCR artifacts (e.g. 0 vs O), normalize to the most likely intended value'

Return: the complete extraction prompt, a test with 3 sample inputs (clean, messy, and edge case), and expected outputs for each.