Book Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs PDF Download - James Phoenix, Mike Taylor

Download ebook ➡ http://get-pdfs.com/pl/book/712751/979

Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs
James Phoenix, Mike Taylor
Page: 422
Format: pdf, ePub, mobi, fb2
ISBN: 9781098153434
Publisher: O'Reilly Media, Incorporated

Download or Read Online Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs Free Book (PDF ePub Mobi) by James Phoenix, Mike Taylor
Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs James Phoenix, Mike Taylor PDF, Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs James Phoenix, Mike Taylor Epub, Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs James Phoenix, Mike Taylor Read Online, Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs James Phoenix, Mike Taylor Audiobook, Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs James Phoenix, Mike Taylor VK, Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs James Phoenix, Mike Taylor Kindle, Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs James Phoenix, Mike Taylor Epub VK, Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs James Phoenix, Mike Taylor Free Download

Overview
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