Enterprise Document Intelligence [Vol. 1 #5nonies] - Nature, plan, execute, synthesize: closing brick 1 with a dispatcher that reads each PDF’s nature and picks the method that fits, fitz, Docling, PaddleOCR, EasyOCR, MinerU or Surya, then folds the outputs into one corpus The post Before Full Agentic RAG: Know How You Decide, and the Parsing Methods You Pick From appeared first on Towards Data Science .
Enterprise Document Intelligence [Vol. 1 #4] - A diagnostic across PDFs and questions, and a map of the techniques the rest of the series will cover The post From Regex to Vision Models: Which RAG Technique Fits Which Problem appeared first on Towards Data Science .
Enterprise Document Intelligence [Vol. 1 #5A] - Document signals (metadata, native TOC, source software) and page-level content (text vs scans, tables, images, columns, page profile) The post Beyond extract_text: The Two Layers of a PDF That Drive RAG Quality appeared first on Towards Data Science .
Enterprise Document Intelligence [Vol. 1 #9A] - Same paper, same question as Article 1.
Enterprise Document Intelligence [Vol. 1 #1] The smallest version of RAG that actually works, on a real PDF, with grounded answers and the source lines highlighted.
Enterprise Document Intelligence [Vol. 1 #5quater] - The other parsers read the words on a page.
Enterprise Document Intelligence [Vol. 1 #6c] - The decisions the parser makes on top of the user string, using the document’s profile: dispatch, activations, full schema, three approaches to deciding what fires, the audit _meta block, and a broker-corpus walkthrough The post Dispatching the Parsed RAG Question: Chunk Strategy, Model Tier, Activations, Audit appeared first on Towards Data Science .
Enterprise Document Intelligence [Vol. 1 #6ter] - Six positions on the question-parsing brick that contradict the mainstream RAG playbook The post The Untaught Lessons of RAG Question Parsing: Structure Before You Search appeared first on Towards Data Science .
The article explains that enterprise document intelligence can be categorized into three distinct corpus types, each requiring a specific architecture. It outlines how to determine the shape of a document collection through three key questions. The piece also discusses the costs associated with building a system for the incorrect corpus type.
The article argues that Retrieval-Augmented Generation (RAG) is only one tool in NLP, and many real-world problems—such as request classification, free‑text matching, table reading, and OCR noise cleaning—are better served by simpler, cheaper techniques. It emphasizes the importance of selecting the appropriate method for each task and highlights the engineering challenge of knowing which technique to apply.
The article discusses how an FAQ can invert the traditional Retrieval-Augmented Generation (RAG) pipeline. It explains that parsing becomes trivial, retrieval functions as a cache, and few‑shot prompting is reframed as a retrieval problem. The piece highlights the practical implications of designing a corpus around an FAQ structure for enterprise document intelligence.
Enterprise Document Intelligence [Vol. 1 #10A] - The escalation cascade and the free, deterministic checks that flag a failed parse before you pay for a deeper one The post Loop Engineering with Adaptive PDF Parsing: Start Cheap, Pay for a Heavier Parser Only When the Page Needs It appeared first on Towards Data Science .
Enterprise Document Intelligence [Vol. 1 #5quinquies] - Same 1974 scanned PDF, two engines.