Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have
unprecedented potential. Because they have been trained on all the public text and images on
the internet they can make useful contributions to a wide variety of tasks. And with the
barrier to entry greatly reduced today practically any developer can harness LLMs and
diffusion models to tackle problems previously unsuitable for automation. With this book
you'll gain a solid foundation in generative AI including how to apply these models in
practice. When first integrating LLMs and diffusion models into their workflows most
developers struggle to coax reliable enough results from them to use in automated systems.
Authors James Phoenix and Mike Taylor show you how a set of principles called prompt
engineering can enable you to work effectively with AI. Learn how to empower AI to work for
you. This book explains: The structure of the interaction chain of your program's AI model and
the fine-grained steps in between How AI model requests arise from transforming the application
problem into a document completion problem in the model training domain The influence of LLM
and diffusion model architecture--and how to best interact with it How these principles apply
in practice in the domains of natural language processing text and image generation and code
