Three parts: understanding what sits inside a language model, using pretrained models for concrete tasks, and training or fine-tuning your own. Alammar has spent years explaining these architectures with drawings and it shows: some diagrams achieve in one page what other books fail to do in a chapter.
What I took from it
The criterion I use most: a good share of what arrives labelled artificial intelligence is really classification or search, and it is solved with embeddings and a threshold, without generating a single word. It is cheaper, it is measured with numbers, it can be audited case by case, and it does not hallucinate. When someone wants an assistant that answers and what they need is to find the right document, saying so early is the biggest favour available.
The explanation of tokens and embeddings as the basis for everything else. Understanding that the model does not see words but numbers explains why it stumbles on a national ID, a product code or a licence plate, and those are half the data in a company.
And topic clustering, which is the fastest way I know to read a thousand comments or a thousand support tickets before deciding what is worth automating.
What I left out
The fine-tuning chapters, which need a graphics card and a labelled dataset that almost no mid-sized company has. That is the least reusable part of the book. And a note on format: a book this visual ages well in its concepts and badly in its screenshots, which no longer match any current tool.