foreword
Transformers and the large language models they made possible, sit at the center of modern AI. They mark one of those rare moments when an elegant theoretical idea meets enormous real-world effects. If specialized hardware is the body of modern computation, transformers are the mind. They are the part that learns, reasons, and creates. Almost every major AI breakthrough we see today—from smart code generation to instant translation and conversational assistants—traces back to a single idea: attention, and the incredible parallelism it unlocked. If you work with AI today, fluency in the language of transformers is no longer optional. It is essential.
But keeping up with this field is no small task. Every few weeks, a new architecture, prompting method, or scaling technique seems to appear. Even experts can find it hard to keep track of what really matters. That is why a book like Transformers in Action feels so timely and valuable. It does not just explain how transformers work. It helps you understand them. It builds the kind of intuition that lets you see these models not as mysterious black boxes but as systems you can reason about, adapt, and improve. That is exactly the kind of understanding Nicole Königstein brings to life.