RAG and Document Processing Systems

Document-heavy workflows need more than a language model prompt. PDFs can contain scanned pages, tables, inconsistent layouts, missing fields, and domain-specific terminology. A dependable system has to combine OCR, deterministic extraction, validation, retrieval, and human review.

Problem

Teams often want to search, summarize, or extract structured data from large collections of documents. RAG can make exploration easier, but strict workflows still need traceability, schema validation, and clear handling of uncertain outputs.

My role

This page summarizes the public, non-confidential architecture I use when thinking about document intelligence systems: design the data flow, choose extraction and retrieval boundaries, validate outputs, and keep the system maintainable for future document types.

Approach

• Ingest PDFs and normalize document metadata.
• Apply OCR or text extraction depending on document quality.
• Split content into chunks that preserve useful context.
• Use retrieval-augmented generation for question answering, summarization, or assisted review.
• Keep deterministic validation around required fields, formats, and business rules.
• Store extracted data and evidence links so users can inspect where an answer came from.

Technologies

The stack depends on the use case, but relevant tools include Python, OCR engines, LlamaIndex-style retrieval abstractions, LLM APIs or local models, MongoDB or relational storage, Docker, and CI/CD pipelines.

Lessons

RAG is most useful when it is part of a wider document processing architecture. The hard work is usually data quality, evaluation, monitoring, and making sure people can challenge or correct the result.