Tutorials Prompt Engineering Tutorial
AI Data Extraction — Complete Guide
AI Data Extraction — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of Prompt Engineering Tutorial on Toolliyo Academy.
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Prompt Engineering Tutorial · Lesson 37 of 100
AI Data Extraction
Prompts → Apps
Prompts · 1 — Basics · ~6 min · Module 4: Structured Outputs
What is this?
Data extraction pulls structured fields from messy text — emails, PDFs, tickets — using prompts tuned for recall and format.
Why should you care?
PromptVerse Invoice Parser extracts vendor, amount, due date from attachment text for AP workflows.
See it live — copy this example
Copy the prompt into ChatGPT, Claude, or your LLM API playground and compare outputs.
Extract from email body to JSON:
{ "vendor": string, "invoice_id": string, "amount_usd": number, "due_date": "YYYY-MM-DD" }
If field missing use null. No guess on amount.
EMAIL: ...What happened?
- Null for missing beats invented amounts.
- ISO date format eases parsing.
- Extraction prompt stays separate from summarization.
Practice next
- Take one real email (redacted).
- Run extraction prompt.
- Compare to human gold labels.
- Add line_items array optional.
- Few-shot one invoice example.
Remember
Separate extract from summarize. Null over guess. Fixed date/number formats.
AP automation
500 invoices/month by email.
Outcome: Extract JSON feeds approval queue.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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