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.
By Kezhan Shi
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 .
By angela shi
Enterprise Document Intelligence [Vol. 1 #9B] - One call wires the four upgraded bricks together, run on a paper, a NIST standard, and a report with a broken TOC The post A Production RAG Pipeline in Action: Every Answer Typed and Cited appeared first on Towards Data Science .
By angela shi
Enterprise Document Intelligence [Vol. 1 #6a] - Why a user question deserves the same parsing as the document, and how it splits into a retrieval brief and a generation brief before either runs The post RAG Questions Need Parsing Too: Turn the User’s String Into Briefs for Retrieval and Generation appeared first on Towards Data Science .
By angela shi
Testing fourteen engines on ninety-three human documents The post I Spent May Evaluating Different Engines for OCR appeared first on Towards Data Science .
By Ida Silfverskiöld
Enterprise Document Intelligence [Vol. 1 #7bis] - Tobi Lütke and Andrej Karpathy named the practice in 2025.
By Kezhan Shi