Towards Data Science August 29, 2026 By Kezhan Shi

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

Read the original on Towards Data Science →

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.

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

Jun 1

RAG Is Not Machine Learning, and the ML Toolkit Solves the Wrong Problem

Enterprise Document Intelligence [Vol. 1 #3] - Why the ML toolkit (hyperparameter sweeps, train/test splits, explainability frameworks) solves the wrong problem, and what to use instead The post RAG Is Not Machine Learning, and the ML Toolkit Solves the Wrong Problem appeared first on Towards Data Science .

By angela 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 16

RAG Questions Need Parsing Too: Turn the User’s String Into Briefs for Retrieval and Generation

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

rag

Towards Data Science

Jul 2

The Untaught Lessons of RAG Question Parsing: Structure Before You Search

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

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

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

One email a morning, machine-written

Related stories

RAG Is Not Machine Learning, and the ML Toolkit Solves the Wrong Problem

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

RAG Questions Need Parsing Too: Turn the User’s String Into Briefs for Retrieval and Generation

The Untaught Lessons of RAG Question Parsing: Structure Before You Search

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

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