Enterprise Document Intelligence [Vol. 1 #7quinquies] - Hallucination is usually garbage-in.
By Kezhan Shi
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.
By Kezhan Shi
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
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 .
By Kezhan Shi
Enterprise Document Intelligence [Vol. 1 #9bis] - Your RAG isn’t hallucinating, it’s answering the wrong context faithfully.
By angela shi
arXiv:2606. 06748v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) reduces but does not eliminate hallucination in large language models.
By Jianru Shen