How do you know if the LLM isn't hallucinating?
Since I'm using it in involved studying sessions, it is totally sensible to cross-check most of what the LLM outputs, since that's part of the learning process anyway!
In my experience learning design, some math, basic android dev with local and private models, they are very reliable if you use them "in the loop" (ie, as part of a process).
During programming, it's extremely easy to verify what the LLM says is correct by running code.
For math, this is a bit more difficult, but so far it's only been surface level math. I know that LLMs are good at teaching concepts and ideas in math, but not at _doing_ math. I don't fully trust an LLM to teach me more advanced math because I have no way of verifying what I learn is
* Correct
* Relevant
* The "right" way of learning it
For design, which I'm currently studying a lot for work and personal projects, it does well. I'm using Claude to help me solidify my understanding by asking it to critique my summaries and quiz me on topics. I know Claude is correct because I'm using it to solidify my understanding and how topics interrelate, but not to learn new topics. I already sort of "intuitively understand" these topics, but am training myself to go a bit deeper.
They work really well, and I really do understand the skepticism, but in practice it's unwarranted.
Sure, that'll happen if you're going into extreme limits of areas of knowledge - but if you're dipping your foot into superficial waters of known areas, it's reasonably fine (and perhaps even more sound than the hotter areas of Wikipedia).
E.g. if you're programming in the most well-known languages (say Python), and you're only asking beginner questions (which is where high pedagogic value lies) it's unlikely that the program will hallucinate all that deeply to mislead you. Yes, you won't know, but simple/mid questions are easy to check.
You shouldn't of course head to LLMs for the most arcane scientific/mathematical knowledge and conclusions; if you do that, that's on you, because offering expertise is not what LLMs are designed for, and the disclaimers all warn against it. But you shouldn't throw out the baby with the bathwater.