Maybe, that really depends on if that task or a very similar task exists in sufficient amounts in its training set. Basically, you could get essentially the same result by searching online for code examples, the LLM might just make it a little faster (and probably introduce some errors as well).
An LLM can only generate text that exists in its training data. That’s a pretty important limitation, which has all kinds of copyright-related issues associated with it (e.g. I can’t just copy a code example from GitHub in most cases).
No, it does not depend on preexisting tasks, which is why I told you those 2 random examples. You can come up with new, never before seen questions if you want to. How to stack a cable, car battery, beer bottle, welding machine, tea pot to get the highest tower. Whatever. It is not always right, but also much more capable than you think.
It is dependent on preexisting tasks, you’re just describing encoded latent space.
It’s not explicit but it’s implicitly encoded.
And you still can’t trust it because the encoding is intrinsically lossy.