The example is kind of opposite of the point GP's trying to make, isn't it? I mean, "cheeseburger" is a very well-known and a very specific thing. You ask a cook to make you one, they'll know what you want. Try asking for "a hamburger with patty topped with sliced cheese and standard condiments", and you'll probably get back a confused look, "er, you mean you want a cheeseburger, right?".
Same pattern happens with LLMs, in my experience. GP says an LLM infrerence is "sort of a decompression process for a lossy copy of the Internet" - but in these terms, if asking it for a cheeseburger means decompressing parts of the latent space around the term "cheeseburger", then asking for "a hamburger with patty topped with sliced cheese and standard condiments" is making it decompress much larger space around multiple terms, and then filter the result out into a semantically relevant subspace, and then run extra inference on that.
If you think about it, the very reason we (humans) give names to things is to avoid having to repeatedly do that decompression and filtering every time we want to refer to a specific thing. We call the modified "hamburger" a "cheeseburger" precisely to avoid having to talk like GP suggests we should talk to LLMs, so I very much think this advice is backwards.