Prompt Engineering For Generative Ai Future Proof Inputs For Reliable Ai Outputs
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Prompt Engineering for Generative AI
Author: James Phoenix
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2024-05-16
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
Prompt Engineering for Generative AI
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
The Essential Guide to Prompt Engineering
Author: Vladimir Geroimenko
language: en
Publisher: Springer Nature
Release Date: 2025-03-17
This book provides a concise yet comprehensive guide to mastering the entire spectrum of prompt engineering, from fundamental concepts to pro-level techniques and essential security considerations. Filled with practical examples and detailed explanations, it delivers actionable knowledge that can be directly applied to AI projects. The guide includes dedicated chapters on key challenges and security issues, equipping readers to overcome significant obstacles they may encounter. It outlines a clear pathway to the art and science of prompt engineering, offering the tools and insights for a successful journey into the rapidly evolving world of generative AI. With its holistic approach and coherent structure, this book is an indispensable resource for AI developers, professionals in related fields, enthusiasts, graduate and undergraduate students, and anyone keen to enhance the efficiency of their interactions with AI models.