Towards Data Science
Jul 15
Most RAG Hallucinations Are Retrieval Failures: How the Retrieval Brick Decides What the Model Can Invent
Enterprise Document Intelligence [Vol. 1 #7quinquies] - Hallucination is usually garbage-in.
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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Towards Data Science
Jul 23
Most RAG Hallucinations Are Extraction Errors: Seven Patterns for a Typed Generation Contract
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.
Towards Data Science
Jul 3
The Untaught Lessons of RAG Retrieval: Cosine Is Not the Foundation
Enterprise Document Intelligence [Vol. 1 #7ter] - Six positions on the retrieval brick that contradict the cosine-first reflex of mainstream RAG The post The Untaught Lessons of RAG Retrieval: Cosine Is Not the Foundation appeared first on Towards Data Science .
arXiv AI
Jun 8
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.
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 .
Towards Data Science
Jul 21
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.
Towards Data Science
Jul 11
That Is Embarrassing: Why Frontier AI Still Makes Things Up, and What to Do About It
The best AI models still hallucinate. These hallucinations are sometimes funny, and sometimes cause actual damage.
Towards Data Science
Jun 23
Retrieval Is Filtering, Not Search: A Mental Model for Enterprise RAG
Enterprise Document Intelligence [Vol. 1 #7A] - Stop searching strings.
arXiv AI
Jun 16
A Unified Definition of Hallucination: It's The World Model, Stupid!
arXiv:2512. 21577v3 Announce Type: replace-cross Abstract: Despite numerous attempts at mitigation since the inception of language models, hallucinations remain a persistent problem even in today's frontier LLMs.
arXiv AI
Aug 5
Quantifying Hallucinations in Language Language Models on Medical Textbooks
arXiv:2603. 09986v3 Announce Type: replace-cross Abstract: Hallucinations, the tendency for large language models to provide responses with factually incorrect and unsupported claims, is a serious problem within natural language processing for which we do not yet have an effective solution to mitigate against.
Towards Data Science
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 .
arXiv AI
Jun 3
Building Reliable Long-Form Generation via Hallucination Rejection Sampling
arXiv:2606. 03628v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved remarkable progress in open-ended text generation, yet they remain prone to hallucinating incorrect or unsupported content, which undermines their reliability.