How do you build a process for extracting data from documents?
Answer out loud or in your head first, then compare.
Answer
After receiving a file, I check its format, size and permissions. I create a job and do the long work outside an ordinary short request. Successive stages read the text, extract the data and check its structure and meaning. Not every stage needs an autonomous agent.
On an invoice, a model can find the amounts, but code can check their total. A missing field shouldn't force a guess. I keep the document version and a pointer to where each value came from, so a person can verify it.
A doubtful result goes for review. Retrying the same job must not create a second business document. I measure quality separately for important fields and file types, because a high average can hide errors in the amount.
Why it matters: A scenario close to experience with extraction and classification.
Possible follow-ups: What about a new document layout? Which stages can be repeated without repeating everything?
How did it go? Be honest — “Not yet” puts the question on your review list.
Start with
- D-P15 · What is idempotency? — Backend, data and system design
- G-P01 · What is RAG? — RAG and retrieval
- H-P01 · How does an AI agent differ from a plain model call? — Agents and workflows
Further reading
- AWS: Using Lambda with Amazon SQS(opens in a new tab)Docs; accessed 22.10.2026. Polling, batches, duplicates and partial batch responses.
- Claude Platform: Structured outputs(opens in a new tab)Docs; accessed 22.10.2026. The schema contract, limits and validation; parameters depend on the API/SDK.