What LLM-augmented extraction means in practice
LLM-augmented extraction uses an LLM to convert document text into structured fields. The classical alternative — template-based extraction with field coordinates per layout — works on standardised forms and collapses on the layout-free document types (contracts, correspondence, free-form claims) that make up the long tail of enterprise document volume. LLM extraction is robust to layout variation because it reads the document the way a human would. The engineering pattern is to prompt the model with the target schema, return the extracted fields plus a per-field confidence score, and route low-confidence fields to human review. MindMap's DocGenie and DocuMage are the two products that ship this pattern in production.
Related terms
Intelligent Document Processing (IDP) →
End-to-end automation of document workflows: capture, classify, extract structured data, validate, route, and integrate into downstream systems.
OCR (Optical Character Recognition) →
The classical step of converting an image of text into machine-readable characters — the foundation layer underneath any document processing pipeline.
Schema-driven extraction →
The pattern where the target output schema is the primary input to the extraction prompt — the LLM is told exactly which fields to find and what type each should be.
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