PromptWork
Extract structured data from text
Turn messy text — emails, PDFs pasted as prose, meeting threads — into clean JSON with an explicit schema and nulls where the truth is missing.
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Your prompt
Extract structured data from the text below.
Return ONLY valid JSON matching this shape:
{ "vendor": "", "invoice_number": "", "amount": 0, "currency": "", "due_date": "YYYY-MM-DD" }
Rules: every value must come from the text — never infer or embellish. Use null for anything the text doesn't state, and copy numbers, dates and names exactly as written (note the original form if you must normalise). If the text contains multiple records, return an array with one object each. After the JSON, list anything ambiguous you had to leave out.
Text:
[paste the email thread, scanned letter or document text here]
Model-agnostic — works in Claude, ChatGPT, Gemini or any AI chat.
Why this prompt works
- →A JSON example is a stronger schema than a description — the model copies its exact keys and value shapes instead of inventing near-misses.
- →"Null for missing, never infer" is the anti-hallucination core: extraction tasks fail worst when blanks get quietly filled with plausible values.
- →The ambiguity list after the JSON gives the judgement calls a home outside the data, so the JSON stays machine-consumable.