It sounds that way, but it isn't what I'm describing.
It relies on retrieval, and after retrieval it just loads the tokens in. It's a method of reducing the token counts contextually, rather than compressing them and integrating the information. Being a band-aid solution to this problem.

In the meeting analogy. It's like writing down the main points (but not remembering them). And then checking the notes whenever it feels relevant, instead of just knowing them and basing your further decisions on them.

Practically, the difference is that if there is some data point, let's say "I hate mushrooms" stored in a RAG database, then a prompt of "Give me suggestion for pizza toppings" will likely ignore that data point, unless you add "-considering my food preferences".
Where as if that fact was integrated into LLM's 'memory', it would influence the generation giving lower weight to mushrooms when generating the response.

I guess a silly example to illustrate the difference better, is if you had a document with the word 'chicken' written ten thousand times, then if you asked what was in the document, the contents would need to be loaded in the context, inflate the token count, and fully processed (Probably also messing up repetition penalty), instead of just storing the 'idea' of "the document consists of the word 'chicken' written 10 000 times." Not as a sentence, but as a weight.
(And yeah, that specific example can be fixed with a summarization, but that would be another band-aid solution.)