Provided that the problem is suited to the strengths of an LLM at all. An example might be a small ai custom trained on documentation for libraries. You ask it a question like "how do I make the background move with parallax effect when you move the cursor". It's a little ambiguous, high-level concept, and probably not a single function.

Small ai: likely makes up a function or suggests a single function which isn't sufficient. Refuses to budge from its answer or apologies and gets confused

Large LLM: able to actually understand the question, combine several functions. If it doesn't work you can tell it why and it fixes it

Because there’s a world of difference between a reinforcement learning trained special purpose model and asking a general purpose large language model to have a go at something.

Because they have an explicit model of chess and specific heuristics for learning chess.

An LLM could have picked up some chess patterns through osmosis, but it can not reason explicitly in the domain.

Because they do completely different things? They literally have nothing to do with each other. Why do planes fly better than ships if ChatGPT can't do math?

Why would a language model be good at playing chess?