How did you verify that it wasn't bogus? Like, when it says "most of the time", or "commonly", or "always", how do you know that's accurate? How do those terms shape your thinking?
> when it says "most of the time", or "commonly", or "always", how do you know that's accurate?
Do you get those words a lot? If you're learning ray-tracing, it's math and code that either works or doesn't. There isn't a lot of "most of the time"?
Same with learning history. Events happened or they didn't. Economies grew at certain rates. Something that is factually "most of the time" is generally expressed as a frequency based on data.
So are you just verifying/factchecking everything it tells you? How is that a good learning experience? And if you don't, you are learning made up stuff, so not great either.
It's a good tool to learn stuff, I'm not trying to argue that, but one has to be fully aware of its shortcomings and put in extra work. With actual tutorials or books you have at least some level of trust.
I mean, I have to verify stuff in human-written tutorials too. Humans are wrong all the time.
A lot of it is just, are its explanations consistent? Does the code produce the expected result?
Like, if you're learning ray-tracing and writing code as you go, either it works or it doesn't. If the LLM is giving you wrong information, you're going to figure that out really fast.
In practice, it's just not really an issue. It's the same way I find mistakes in textbooks -- something doesn't quite add up, you look it up elsewhere, and discover the book has a typo or error.
Like, when I learn with an LLM, I'm not blindly memorizing isolated facts it gives me. I'm working through an area, often with concrete examples, pushing back on what seems confusing, until getting to a state where things make sense. Errors tend to reveal themselves very quickly.
