Can’t say that I struggled to understand pointers but if GPT helped you conceptualize em that’s good. I really don’t see much utility in even the current iterations of these LLMs. Take copilot for example, ultimately all it actually helps with is boilerplate which if you are writing enough for it to be meaningfully helpful you can have a fancy IDE live template or just a plain old snippet.

Theres a lot of interesting things it could be doing like checking if my documentation is correct or the like but all it does is shit I could do myself with less hassle.

There’s also the whole issue of LLMs having no concept of anything. You aren’t having a conversation, it just spits out the words it thinks are most likely to occur in the given context. That can be helpful for extremely generic questions it’s been trained on thanks to Stack Overflow but GPT doesn’t actually know the right answer. It’s like really fancy autocorrect based on the current context. What this means is you absolutely cannot trust anything it says unless you know enough about the topic to determine what it outputs is accurate.

To draw a comparison to written language (hopefully you don’t know Japanese) is 私 or 僕 “I”? Can you confidently rely on auto correct to pick the right one? Probably not cause the first one わたし (watashi) is “I” and the second ぼく (boku) is also “I” (more boyish). Trusting an LLMs output without being able to ensure it’s accuracy is like trusting auto correct to use the right word in a language you don’t know. Sure it’ll work out fine generally but when it fails you don’t have the knowledge to even notice.

Because of these failings I don’t see much utility in LLMs especially seeing as the current obsession is chat apps geared at the general public to fool around with.