What this prompt does
This prompt asks the AI to build a document processing pipeline for [document_types] that extracts [extracted_data]. Documents arrive via API upload, email forwarding, or [intake_method]; a classification step using [model_provider] identifies the type before processing; text comes from OCR via [ocr_provider] for images and native parsing for PDFs; and an LLM extracts structured data validated against an [output_schema]. Each field gets a confidence score, and low-confidence fields are flagged for human review.
The structure works because extraction without confidence scoring and a review step is a trap — it looks accurate until it silently isn't. By routing flagged documents to a human-in-the-loop interface, the pipeline keeps accuracy high in production. Batch processing of [batch_size] documents concurrently, storage in [database] with a full audit trail, an integration API pushing to [downstream_systems], and accuracy benchmarks on [test_set_size] test documents make it operational, implemented in [language]. Classifying each document with [model_provider] before extraction also matters: an invoice and a contract need different field sets, so identifying the type first lets the pipeline apply the right schema instead of forcing one extraction template across everything.
When to use it
- You process invoices, receipts, contracts, or similar
[document_types]and want structured fields extracted automatically. - You need confidence scoring so uncertain extractions get reviewed instead of trusted blindly.
- You want a human-in-the-loop step for the documents the model is unsure about.
- You are pushing extracted data into
[downstream_systems]like accounting or ERP and need a clean integration API. - You handle volume and need batch processing of
[batch_size]documents at once. - You require an audit trail in
[database]for compliance or debugging.
Example output
Expect an intake layer ([intake_method]), a classifier using [model_provider], OCR via [ocr_provider] plus PDF parsing, an LLM extractor validating against [output_schema], per-field confidence scoring, a review interface for flagged docs, batch processing at [batch_size], [database] storage with audit trail, and a push API to [downstream_systems]. Accuracy benchmarks run on [test_set_size] documents.
Pro tips
- Define
[output_schema]strictly with Zod or Pydantic; loose schemas let the model return plausible-but-wrong shapes that pass silently. - Tune confidence thresholds so genuinely uncertain fields route to review — too lax and bad data flows through, too strict and humans drown.
- Make
[extracted_data]explicit field by field; "line items, tax, total" extracts far more reliably than "the important stuff". - Do not skip the human-in-the-loop step; it is the part teams forget and the reason production accuracy holds up over time.
- Use a representative
[test_set_size]set with real document variety — clean samples overstate accuracy you will not see in the wild. - Keep the audit trail in
[database]complete; when an extraction is wrong, you want the source document and confidence scores to trace it.
