Enterprise Document Intelligence: A Series on Building RAG Brick by Brick, from Minimal to Corpus scale

The standard Retrieval-Augmented Generation (RAG) recipe of chunking, vector storage, and top-k retrieval often fails in enterprise deployments, leading to untrustworthy results. This approach neglects crucial elements like domain knowledge, document structure, and the actual needs of experts. A more effective method focuses on a verifiable pipeline of document parsing, question parsing, retrieval, and generation that prioritizes information extraction over creative writing. This shifts the goal from augmenting an LLM's memory to strictly grounding its answers in retrieved text with clear citations. This series proposes building such a system, arguing that even a simple script built on these principles can be more useful than complex but fundamentally flawed production systems.