> There's something incredibly peaceful about being in the hands of an expert you trust. [...] AI can absolutely shatter that feeling in an uncomfortable way [...] but I don't know if I can fully trust AI either. This really is key. We know we can't trust the AI, but at the same time we're also more comfortable asking the AI for clarifications or confronting it. Not having a time-bound appointment or paying by the ho…
I have multiple LLM subscriptions at any given time, plus an array of local models. When I ask a question outside of my domain of expertise I like to ask all of the LLMs I have access to. I also create separate sessions and ask the same question multiple ways. It’s revealing to see how many different and contradictory answers I get, most of which are presented confidently. The last time I ran a medical question throu…
It did great, generated a report on the assessed business that was incredibly detailed and plausible.
Then I started running tests and getting into the details, and found that if you ran the same report on the same data, it generated completely different, still very plausible, results. I could run the same source data through the assessment process 10 times and get 10 very different results. We had to can the project and go a different route.
LLMs are designed to produce plausible results, not factual results. We can fix this when using them for software dev by using linters and tests (though we've all had the experience where the LLM invents an API endpoint). I would not trust raw LLM output in any situation where that kind of testing and verification capability isn't present.