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Document Extraction with AI

Design a checked extraction workflow for fictional invoices, including missing fields and arithmetic validation.

3 reading lessons · about 30 min ·written by MTT

The idea

Document extraction turns source content into named fields. Define types, allowed formats and missing-value behavior before using a model or OCR tool. Keep the source location for important values. A field may be present but unreadable; distinguish that from a field that was never supplied.

Worked example

A fictional invoice includes invoice number, currency and grand total, but no due date. The extracted due date should remain unknown. A model must not infer it from a standard payment policy unless that inference is explicitly separate from the extracted source fields.

Try it

Draw a fictional invoice and a schema for five fields. Specify a missing-value representation and the supporting source line for each field. Add one unreadable field and write the review status it should receive instead of a fabricated value.

Lesson 1 of 3 · About 10 min

Define an extraction contract

Check your understanding

Course quiz

Finish the course to unlock the quiz

Complete all 3 lessons and 5 questions open up here. You have 3 to go.

What you will learn

  • Define field types and source evidence
  • Validate dates, currency and totals
  • Route ambiguous extractions for review

Before you start

Prerequisites
Basic document and spreadsheet familiarity. Use fictional documents only.
Cost
Free introductory reading lessons, exercises and quiz. Optional third-party tools, hosting or AI subscriptions may cost money.

Original introductory lessons and assessment by Master the Trick. Estimated times include the suggested exercises.