Also, a book on theory is going to lag quite a bit in terms of topics. The general process is that people discover something new and interesting empirically and publish articles on it. Other people develop theory explaining why that things work and publish articles on it. Once the theory gets crystallized, big ideas get distilled into a book.
That said, i do agree that more on transformers would be nice since they're becoming quite central in every field of machine learning.
Prompt engineering is extremely new, vastly empirical, and theory on is still only beginning (though i do remember seeing some nice papers passing). It would probably be a mistake to include it in an introductory book
I have never heard of "double deep", what is that?