Document Extraction Schema Prompt
Designs the schema and validation rules before you build extraction.
/ quick answer
Run first when automating any document intake process. Designs the schema and validation rules before you build extraction.
Act as a data engineer designing document extraction. For the document type below, return:
1. SCHEMA — every field with type, required/optional, and normalisation rule (dates, currency, IDs).
2. VALIDATION RULES — arithmetic checks, cross-field consistency, external lookups.
3. AMBIGUITY CASES — the 5 places extraction most often goes wrong for this document type.
4. HUMAN QUEUE CRITERIA — exactly which failures a human must review.
5. TEST SET — 8 document variations to test against, including 3 malformed ones.
Document type: {{DOC_TYPE}}
Downstream system: {{SYSTEM}}
Volume: {{VOLUME}}1. SCHEMA — supplier_name (string, required), vat_id (string, optional, uppercase no spaces), total (decimal, required)...
/ frequently asked
What does the Document Extraction Schema Prompt prompt do?
Run first when automating any document intake process.
Which AI models work with this prompt?
It is model-agnostic: it works with any capable general model. Replace the bracketed variables with your own context before running it.
How do I use this prompt?
1) Copy the prompt with the copy button. 2) Replace every bracketed variable with your real details. 3) Paste it into your AI assistant. 4) Compare the answer with the example output and ask for changes if needed.
What output should I expect?
1. SCHEMA — supplier_name (string, required), vat_id (string, optional, uppercase no spaces), total (decimal, required)...
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →Document Extraction Agent
A document extraction agent reads unstructured files — PDFs, scans, emails — and returns validated structured data.
- →No-Code Automation
Building business workflows visually without writing code.
- →Entity Extraction
Pulling structured entities (people, places, orgs, dates) from text.
- →Research Automation
Research automation turns a question into a sourced, structured answer using search, retrieval, extraction and synthesis agents.
Related workflows
Turn this into a repeatable process.
- →Automate Document Intake End-to-End
From inbox to validated record with humans only on exceptions.
- →Automate Invoice Extraction to Sheets
Turn PDF invoices into structured rows without a bookkeeper.
- →Build a Research Automation Pipeline
Question in, sourced structured brief out — on a schedule.
Related tool stacks
The tools that run it in production.
- →Document AI Stack
Turn PDFs and scans into validated records with a human exception queue.
- →Browser Automation Stack
Run browser agents on a schedule with credentials, retries and screenshots.
- →Research Automation Stack
Search, fetch, extract and synthesise sourced briefs on a schedule.
- →Crypto Automation Stack
Automation platform, data APIs, alerting and optional wallet execution — the operational layer for monitoring and recurring actions.
Related prompts
Reusable prompts for this job.
- →Browser Automation Task Spec Prompt
Converts a manual click-path into a reliable automation spec.
- →Sourced Research Brief Prompt
Produces a structured brief where every claim carries a citation.
- →No-Code Automation Spec Writer
Turn a vague 'I want to automate X' into a buildable scenario spec for Make / n8n / Zapier.
- →Prompt Improver Prompt
Iterate a weak prompt into a strong one using best practices.
Comparisons & alternatives
Pick between the options.
- →Best AI Workflow Automation Tools: n8n vs Zapier vs Make
The three tools most operators consider for AI workflow automation — compared on pricing, AI integration and technical flexibility.
- →Browser Agent vs API Automation
APIs win whenever they exist; browser agents exist for the systems that never gave you one.