Towards Data Science July 23, 2026 By Kezhan Shi

Most RAG Hallucinations Are Extraction Errors: Seven Patterns for a Typed Generation Contract

Read the original on Towards Data Science →

Enterprise Document Intelligence [Vol. 1 #8ter] - Naming the RAG error correctly matters: model reads the context, so a wrong answer is an extraction error, not a hallucination.

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Most RAG Hallucinations Are Retrieval Failures: Fix Retrieval, Not the Prompt

Enterprise Document Intelligence [Vol. 1 #7quinquies] - Hallucination is usually garbage-in.

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Prompt Engineering Isn’t Enough: How Four Bricks of Context Engineering Stop RAG Hallucinations

Enterprise Document Intelligence [Vol. 1 #9bis] - Your RAG isn’t hallucinating, it’s answering the wrong context faithfully.

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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 .

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Jul 4

Stop Returning Text from RAG: The Typed Answer Contract That Prevents Hallucination

Enterprise Document Intelligence [Vol. 1 #8A] - The schema is the contract: every field is a question the pipeline asks the model, and every answer is checkable The post Stop Returning Text from RAG: The Typed Answer Contract That Prevents Hallucination appeared first on Towards Data Science .

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Evidence Graph Consistency in Retrieval-Augmented Generation: A Model-Dependent Analysis of Hallucination Detection

arXiv:2606. 06748v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) reduces but does not eliminate hallucination in large language models.

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