AI document processing enables automated extraction of structured data from unstructured documents. Key technologies: 1) OCR with Google Document AI or AWS Textract for layout-aware text extraction - preserves tables, forms, and structure better than simple OCR. 2) Vision LLMs (GPT-4 Vision, Claude, Gemini) for understanding document structure and extracting fields from complex layouts. 3) LlamaParse for sophisticated PDF parsing that preserves tables and formatting for RAG. Prompt engineering for extraction: "Extract the following fields as JSON: invoice_number, date, vendor_name, line_items (array with description, quantity, unit_price, total), total_amount. If a field is not found, use null." 4) Validation pipeline: after extraction, validate against expected formats (dates, amounts, required fields), flag anomalies for human review. 5) For high-volume production: use async processing with queues, implement confidence scoring, route low-confidence extractions to human review. Accuracy benchmarks: GPT-4V achieves 95%+ on standard invoice extraction vs 80% for traditional template-based approaches.