Enterprise Document Intelligence [Vol.1 #B2] - The FAQ inverts every brick of the standard RAG pipeline. Parsing is trivial, retrieval doubles as a cache, and few-shot prompting becomes a retrieval problem too
Rag Architecture
A framework for building RAG pipelines that introduces complexity in response to observed failure modes, from lexical and hybrid search to reranking and agentic information seeking
Enterprise Document Intelligence [Vol.1 #2D] - What data scientists say when asked, what the model actually does under the hood, and why the honest answer changes your architecture decisions in enterprise RAG
One Document Type, a Million Files: Structured Extraction into the SQL Table RAG Queries
Large Language ModelEnterprise Document Intelligence [Vol.1 #14C] - One hour with two people, six to ten fields, and the two signals that separate a real column from one that will break a filter later
Enterprise Document Intelligence [Vol.1 #M3] - The ten positions the series argues from, and the map of every article that argues them
Parse the Folder, Not Just the PDFs: The Relational Tables RAG Needs on a Case File
Large Language ModelEnterprise Document Intelligence [Vol.1 #14D] - The index lists what the case type demands before any folder is opened, and the two questions worth building for are not retrieval questions at all
Multi-Document RAG: A Folder of Unrelated PDFs Is One Long Document with a Nested Outline
Large Language ModelEnterprise Document Intelligence [Vol.1 #14B] - No shared fields means no index to build. One summary line per file plus each file’s own table of contents, and retrieval routes down two levels
Enterprise Document Intelligence [Vol.1 #7sexies] - The unit of retrieval doesn’t have to be a page or a paragraph. When the corpus carries tables, each body row with its column headers is a chunk in its own right, and it’s often the one row the reader asked about
Enterprise Document Intelligence [Vol.1 #14A] - Three questions tell you which shape a document collection has, and each shape wants a different architecture
Loop Engineering for RAG: The Small Loops Inside Each Step, the Big Loops Across the Pipeline
Large Language ModelEnterprise Document Intelligence [Vol.1 #13bis] - The four bricks return useful results most of the time. Loop engineering is what the system does the rest of the time: when retrieval misses, when generation fails the schema, when the listing comes back incomplete, when an API call times out. Three control surfaces (trigger, termination, recovery) and one rule that separates a useful loop from a spinning one