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Prompt engineering for generative AI : = future-proof inputs for reliable AI outputs at scale /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Prompt engineering for generative AI :/ James Phoenix and Mike Taylor.
Reminder of title:	future-proof inputs for reliable AI outputs at scale /
remainder title:	Prompt engineering for generative artificial intelligence :
Author:	Phoenix, James
other author:	Taylor, Mike
Published:	Sebastopol, CA :O'Reilly, : 2024.,
Description:	xvii, 401 p. :col. ill. ; : 24 cm.;
Notes:	Includes index.
Subject:	Natural language generation (Computer science) -
ISBN:	9781098153434 (pbk.) :

Prompt engineering for generative AI : = future-proof inputs for reliable AI outputs at scale /
Phoenix, James(Computer engineer),

Prompt engineering for generative AI :future-proof inputs for reliable AI outputs at scale /Prompt engineering for generative artificial intelligence :future-proof inputs for reliable AI outputsJames Phoenix and Mike Taylor. - 1st ed. - Sebastopol, CA :O'Reilly,2024. - xvii, 401 p. :col. ill. ;24 cm.

Includes index.

The five principles of prompting --

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. --

ISBN: 9781098153434 (pbk.) :NT2601

LCCN: 2024391019Subjects--Topical Terms:

1340892
Natural language generation (Computer science)

LC Class. No.: QA76.9.N38 / P463 2024

Dewey Class. No.: 006.3/5

Prompt engineering for generative AI : = future-proof inputs for reliable AI outputs at scale /
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300 $a xvii, 401 p. : $b col. ill. ; $c 24 cm.
500 $a Includes index.
505 0 0 $t The five principles of prompting -- $t Introduction to large language models for text generation -- $t Standard practices for text generation with ChatGPT -- $t Advanced techniques for text generation with LangChain -- $t Vector databases with FAISS and Pinecone -- $t Autonomous agents with memory and tools -- $t Introduction to diffusion models for image generation -- $t Standard practices for image generation with Midjourney -- $t Advanced techniques for image generation with Stable diffusion -- $t Building AI-powered applications.
520 # $a 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. -- $c Amazon.com.
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650 # 0 $a Natural language processing (Computer science) $3 641811
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