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Towards Data Science

Noisy Text in RAG: Typos, OCR, and the Gap Classical Spell-Check Leaves

The article discusses how noisy text—stemming from user typos, rapid transcription errors, and OCR character mistakes—poses challenges for Retrieval-Augmented Generation (RAG) systems. It explains that traditional spell-checking only addresses one type of error, while embeddings are needed to handle the remaining noise. The piece highlights the need for more robust solutions in enterprise document intelligence.

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Towards Data Science
Aug 29

RAG Is Not the Whole Toolkit: The NLP Techniques Real Problems Still Need

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
ragcomputer-visionnlp
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Towards Data Science
Jun 9

10 Common RAG Mistakes We Keep Seeing in Production

Enterprise Document Intelligence [Vol. 1 #4bis] - A coauthor note on the brick-by-brick pitfalls that justified the four-brick split, before Part II walks the fixes The post 10 Common RAG Mistakes We Keep Seeing in Production appeared first on Towards Data Science .

By Kezhan Shi
rag
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Towards Data Science
Aug 20

Three Kinds of RAG Corpus, and What It Costs to Build for the Wrong One

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.

By angela shi
rag
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Towards Data Science
Jul 16

Context Engineering for RAG Question Parsing: From a Raw Question to Typed Fields That Steer Retrieval and Generation

Enterprise Document Intelligence [Vol. 1 #6quater] - Question parsing takes one messy string and writes four typed pieces, each read by a different downstream call The post Context Engineering for RAG Question Parsing: From a Raw Question to Typed Fields That Steer Retrieval and Generation appeared first on Towards Data Science .

By Kezhan Shi
rag
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