Название: Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs at Scale (5th Early Release)Автор: James Phoenix, Mike Taylor
Издательство: O’Reilly Media, Inc.
Год: 2024-03-13
Страниц: 440
Язык: английский
Формат: epub
Размер: 78.0 MB
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.
The rapid pace of innovation in generative AI promises to change how we live and work, but it’s getting increasingly difficult to keep up. The number of AI papers published on arXiv is growing exponentially, Stable Diffusion has been among the fastest growing open-source projects in history, and AI art tool Midjourney’s Discord server has tens of millions of members, surpassing even the largest gaming communities. What most captured the public’s imagination was OpenAI’s release of ChatGPT, which reached 100m users in two months, making it the fastest-growing consumer app in history. Learning to work with AI has quickly become one of the most in-demand skills. Everyone using AI professionally quickly learns that the quality of the output depends heavily on what you provide as input. The discipline of prompt engineering has arisen as a set of best practices for improving the reliability, efficiency, and accuracy of AI models.
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
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