My point was that you could pass all the information contained in an embedding as a text prompts into a model, rather than using it directly as an input vector, and an LLM could probably figure out how to use it even if the way you chose to deliver those embeddings was doing a numpy.savetxt and then sending the resulting string is as a prompt. I also pointed out that you could if your really wanted to write a network to convert an embedding to some sort of semantically meaningful word soup that stores the same amount of information. It's basically a pointless bit of trivia which illustrates a fun idea.

I'm not particularly interested in arguing whatever you think I want to argue. I made a pedantic aside that technically you can represent the same information in different formats, including representing embedding as text, and that a transformer based architecture would be able to find patterns it it all the same. I don't see anything to argue here, it's just a "you could also do it this way, isn't that neat." It's sort of the nature of a public forum; you made a post that made me think something, so I hit reply and wrote down my thoughts, nothing more.