Description: Prompt Engineering for Generative Ai : Future-proof Inputs for Reliable Ai Outputs, Paperback by Phoenix, James; Taylor, Mike, ISBN 109815343X, ISBN-13 9781098153434, Brand New, Free shipping in the US 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 betweenHow AI model requests arise from transforming the application problem into a document completion problem in the model training domainThe influence of LLM and diffusion model architecture—and how to best interact with itHow these principles apply in practice in the domains of natural language processing, text and image generation, and code
Price: 59.34 USD
Location: Jessup, Maryland
End Time: 2024-11-16T17:14:29.000Z
Shipping Cost: 0 USD
Product Images
Item Specifics
Return shipping will be paid by: Buyer
All returns accepted: Returns Accepted
Item must be returned within: 14 Days
Refund will be given as: Money Back
Return policy details:
Book Title: Prompt Engineering for Generative Ai : Future-proof Inputs for Re
Number of Pages: 350 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
Subject: Machine Theory, Natural Language Processing, Neural Networks
Item Height: 0.9 in
Type: Textbook
Item Weight: 25.6 Oz
Subject Area: Computers
Author: James Phoenix, Mike Taylor
Item Length: 9.3 in
Item Width: 7.6 in
Format: Trade Paperback