I “rely” on weather report apps but expect inaccuracies. I rely on medical tests results but expect FDRs of about .05. In contrast, I expect financial apps to have very low FDRs.
I use Claude as a tool to explore research areas and topics.
Here is an example of a recent prompt: “Please provide references to papers that have reported swelling of brain tissue (increase in volume) in neurodegenerative disease such as Alzheimer’s using MRI methods.”
After a back-and-forth I got just what I needed, and more easily than a PubMed query—-two references that demonstrate early-stage volume increases in some parts of the brain of adults with presenilin 1 mutations.
It's instead providing references because enough of the OTHER stuff in the training data (which is entirely undocumented but we can assume includes a scrape of the web, blog posts, Reddit, other papers etc) talked about those papers in a way that provided context on the paper and enough of a title/URL that Claude could usefully return that.
By its nature then you'll only ever find references to VERY popular papers that way. That might be what you want! But you shouldn't expect Claude to be able to dig into the lesser known research around a topic based exclusively on its model weights.
For that you'd be much better off with a system like Gemini Deep Research: https://blog.google/products/gemini/google-gemini-deep-resea...
I don't think parent is arguing for just connecting an LLM with that prompt to random end-users, they're talking about using that prompt for their own work, which I'd assume goes through the usual process of being verified and so on.
You're essentially gambling on where in the embedding space your end users are going to query: you might get lucky or you might not.
You're also relying on functionality they're actively trying to degrade during post-training (repeating training data text verbatim). Some LLM providers will even actively filter it: https://ai.google.dev/gemini-api/docs/troubleshooting?lang=p...