In one sentence
Extraction is pulling specific pieces of information out of unstructured text or documents.
The simple model
You have messy input, an email, a PDF invoice, a form filled in freely, and you need particular values from it: the order number, the amount, the delivery date, the customer name.
Extraction reads the input and pulls those values into named fields. It turns unstructured data into structured data the rest of the workflow can act on.
Why you care
Extraction is often the bridge that makes the rest of a workflow simple. Once the important values are in clean fields, ordinary rules can take over: check the amount, match the order, route by type. The hard, interpretive part is done, and the reliable, rule-based part follows.
It is one of the highest-value uses of AI in document-heavy work, exactly the situations where people currently re-read the same documents by hand.
Extraction can be wrong, so validate
Because extraction is interpretation, it can be wrong, especially on messy or unusual documents. A smudged number, an unexpected layout, a missing field. The danger is that an extracted value looks clean and confident even when it is wrong.
So extracted values should be validated before the workflow acts on them: check formats, ranges, and matches against known records. This is the extract then validate pattern, and it is what makes extraction safe to build on.
A small example
A team extracted supplier, amount and purchase-order number from invoice PDFs. Most were clean. A validation step confirmed the amount was a number in a sensible range and the purchase-order number matched a real order. The few extractions that failed validation went to a person, so wrong values never reached payment.
What this does not solve
Extraction gets values out of documents; it does not guarantee they are correct, and it does not decide what to do with them. Validation catches many errors but not all. Keep a path for the cases that fail checks or look unusual.
