AI Document Extraction Pipeline Blueprint

The problem

Building document extraction pipelines often results in "hallucination debt" where models guess missing values or fail silently on messy scans. Developers struggle to bridge the gap between a raw LLM prompt and a reliable, production-ready system that handles validation, human-in-the-loop, and accuracy tracking.

What it does

• Generates strict JSON target schemas including metadata for page references and extraction confidence.
• Designs multi-stage ingestion workflows covering OCR branching, classification, and page splitting.
• Produces ready-to-paste extraction prompts with built-in protection against prompt injection and hallucination.
• Defines deterministic validation layers to check arithmetic (e.g., line item sums) and format consistency in code.
• Blueprints an evaluation strategy with specific golden sets and field-level accuracy bars.

Frameworks & tools

Compatible with LLM APIs (OpenAI, Anthropic), LangChain, Unstructured.io, Tesseract, AWS Textract, and various IDP no-code tools.

Why this beats prompting it yourself

Most DIY prompts fail to account for document-specific risks like hidden text attacks or mathematical drift. This skill enforces an architected approach that treats documents as untrusted data and ensures every extraction is validated by code rather than model intuition.

Use cases

• Extracting line-item data from varying supplier invoice layouts into an ERP.
• Parsing complex medical or insurance intake forms into structured patient records.
• Digitizing legal contracts to identify key terms, dates, and obligations.
• Building resume parsers for ATS systems that require high precision on work history.

Known limitations

Does not provide the final implementation code or API integrations. It focuses on the architectural blueprint, schema design, and prompt engineering.