Towards Data Science August 30, 2026 By Kezhan Shi

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

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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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Towards Data Science.

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.

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rag computer-vision nlp

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

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

Towards Data Science

Jun 30

Context Engineering for RAG: The Four Typed Inputs Behind Every RAG Answer

Enterprise Document Intelligence [Vol. 1 #7bis] - Tobi Lütke and Andrej Karpathy named the practice in 2025.

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llms rag

Towards Data Science

Jun 30

Context Engineering for RAG : The Four Typed Inputs Behind Every RAG Answer

Enterprise Document Intelligence [Vol. 1 #7bis] - Tobi Lütke and Andrej Karpathy named the practice in 2025.

By Kezhan Shi

llms rag

Towards Data Science

May 30

Embeddings Aren’t Magic: The Predictable Failure Modes of RAG Retrieval

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By angela shi

rag

One email a morning, machine-written

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

10 Common RAG Mistakes We Keep Seeing in Production

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

Context Engineering for RAG: The Four Typed Inputs Behind Every RAG Answer

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Embeddings Aren’t Magic: The Predictable Failure Modes of RAG Retrieval