Extract Data From Text with AI
Free text in, named fields out. The extraction prompt pattern that turns any unstructured text into consistent, parseable records.
View Resource →Extraction Prompt Generator resources
Build prompts that extract defined fields from unstructured text — emails, invoices, tickets, résumés.
Extract Invoice Data with AI
Invoice number, vendor, dates, total, currency — extracted into clean fields with strict no-inference rules, ready for accounts payable.
View Resource →Missing Data in AI Extraction — Null, Unknown, or Skip
The most consequential setting in any extraction prompt: what the model does when the field isn't in the text. Four behaviors, and when each is right.
View Resource →Extract Contract Information with AI
Parties, effective date, term, payment, termination notice, governing law — key terms into a contract register, with "unknown" marking every gap loudly.
View Resource →Extract Fields From Emails with AI
Sender, company, request, deadline — out of emails with quoted replies and signature blocks, using guidance that knows how email is actually read.
View Resource →Extract Action Items From Meeting Notes
Decisions actually made, commitments actually given — extracted from fragmentary meeting notes that never label their action items.
View Resource →Extract Product Review Insights with AI
Pros, cons, feature requests, rating — review text into feedback-board fields, with experienced-vs-wished kept strictly apart.
View Resource →Extract Resume Information with AI
Candidate name, current role, years, skills, education — résumés into consistent screening records, with inference kept on a short leash.
View Resource →Extract Support Ticket Metadata with AI
Product, issue summary, stated severity, steps already tried — ticket fields extracted from free-text customer messages, without the model's own judgment leaking in.
View Resource →Information Extraction Prompt — the Anatomy
The six sections a reliable extraction prompt needs: source guidance, field definitions, extraction rules, missing-data behavior, ambiguity policy, example.
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