The example in the article seems less like a RAG issue and more like a prompt issue. The fact that LLM's seeming can't disappoint the user and have to assume they are correct is what is leading to the misinformation in articles like this.

That said there is a clear vulnerability in LLM's not being able to judge whether information is true or not especially when pulling from the web.