Prompt engineering for generative AI (Record no. 49538)

000 -LEADER
fixed length control field 02070 a2200229 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 260324b ii ||||| |||| 00| 0 mal d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9789355424655
080 ## - UNIVERSAL DECIMAL CLASSIFICATION NUMBER
Universal Decimal Classification number 006.35 PHO
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Phoenix, James
245 ## - TITLE STATEMENT
Title Prompt engineering for generative AI
Remainder of title : Future-Proof inputs for reliable AI outputs
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc O’Reilly Media Inc.,
Date of publication, distribution, etc 2024.
Place of publication, distribution, etc California:
300 ## - PHYSICAL DESCRIPTION
Extent 401p.
505 ## - FORMATTED CONTENTS NOTE
Formatted contents note The five principles of prompting -- Introduction to large language models for text generation -- Standard practices for text generation with<br/>ChatGPT -- Advanced techniques for text generation with LangChain -- Vector databases with FAISS and Pinecone -- Autonomous agents with<br/>memory and tools -- Introduction to diffusion models for image generation -- Standard practices for image generation with Midjourney --<br/>Advanced techniques for image generation with Stable diffusion -- Building AI-powered applications
520 ## - SUMMARY, ETC.
Summary, etc 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.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Natural language generation (Computer science)
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Artificial intelligence--Computer programs
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Natural language processing (Computer science)
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element AI
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Taylor, Mike
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Item type Books
Source of classification or shelving scheme Dewey Decimal Classification
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Total Renewals Full call number Barcode Date last seen Date last borrowed Copy number Cost, replacement price Price effective from Koha item type Public note Checked out
    Dewey Decimal Classification     Main Library Main Library (First Floor, Rack No.01) 24/03/2026 48 1875.00 2 1 006.35 PHO 38497 11/10/2026 20/07/2026 2 1875.00 24/03/2026 Books Recommended by Prof. Satish Krishnan  
    Dewey Decimal Classification     Main Library Main Library (First Floor, Rack No.01) 24/03/2026 48 1875.00 1 1 006.35 PHO 38496 25/03/2026 25/03/2026 1 1875.00 24/03/2026 Books Recommended by Prof. Satish Krishnan 02/11/2026