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Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI

Description: 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

Price: 62.68 USD

Location: East Hanover, New Jersey

End Time: 2024-12-01T21:51:37.000Z

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Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AIPrompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI

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Return shipping will be paid by: Buyer

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EAN: 9781098153434

UPC: 9781098153434

ISBN: 9781098153434

MPN: N/A

Book Title: Prompt Engineering for Generative AI: Future-Proof

Number of Pages: 422 Pages

Publication Name: Prompt Engineering for Generative Ai : Future-Proof Inputs for Reliable Ai Outputs

Language: English

Publisher: O'reilly Media, Incorporated

Publication Year: 2024

Item Height: 0.9 in

Subject: Machine Theory, Natural Language Processing, Neural Networks

Item Weight: 25.6 Oz

Type: Textbook

Subject Area: Computers

Item Length: 9.3 in

Author: James Phoenix, Mike Taylor

Item Width: 7.6 in

Format: Trade Paperback

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