Prompt Engineering for Generative AI by James Phoenix (.ePUB)

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File Size: 33.5 MB

Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs by James Phoenix, Mike Taylor
Requirements: .ePUB reader, 33.5 MB
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. All of the code in this book is in Python and was designed to be run in a Jupyter Notebook or Google Colab notebook. The concepts taught in the book are transferable to JavaScript or any other coding language if preferred, though the primary focus of this book is on prompting techniques rather than traditional coding skills. The code can all be found on GitHub, and we will link to the relevant notebooks throughout. It’s highly recommended that you utilize the GitHub repository and run the provided examples while reading the book.
Genre: Non-Fiction > Tech & Devices

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