Large Language Models and Prompt Engineering: A Comprehensive Guide with Practical Applications
暫譯: 大型語言模型與提示工程：結合實務應用的完整指南

Kuhail, Mohammad Amin, Tubishat, Mohammad, Kohail, Sarah

Large Language Models and Prompt Engineering: A Comprehensive Guide with Practical Applications

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商品描述

This book provides a structured and accessible introduction to the theory, design, and real-world use of large language models (LLMs). As generative AI systems become embedded across industries, this book offers readers a balanced perspective that combines technical foundations with practical guidance and critical reflection.

Beginning with the fundamentals of LLMs and prompt engineering, this book progressively explores advanced prompting techniques, application and interaction frameworks, and the underlying architectures that power modern language models. Dedicated chapters examine how LLMs are being applied in high-impact domains such as healthcare, customer support, and education, highlighting design principles, implementation considerations, and illustrative case studies. The book also addresses model fine-tuning, evaluation methods, and the ethical, social, and regulatory challenges associated with deploying LLMs at scale.

This book supports readers in:

- Understanding prompt engineering strategies and application frameworks

- Gaining insight into contemporary LLM architecture and capabilities

- Designing and evaluating LLM-based systems across real-world domains

- Considering ethics, safety, and human-centered aspects of LLM deployment

This book is a practical, accessible resource for those seeking to work thoughtfully and responsibly with LLMs in both academic and applied settings.

商品描述(中文翻譯)

本書以結構化且易於理解的方式，介紹大型語言模型（large language models, LLMs）的理論、設計與實務應用。隨著生成式 AI 系統逐漸融入各個產業，本書提供兼具平衡性的觀點，結合技術基礎、實務指引與批判性思考。

本書從 LLM 與提示工程（prompt engineering）的基礎開始，逐步探討進階提示技術、應用與互動框架，以及驅動現代語言模型的底層架構。書中特別安排章節，深入檢視 LLM 在醫療保健、客戶支援與教育等高影響力領域中的應用，並說明設計原則、實作考量與具體案例研究。本書也涵蓋模型微調（fine-tuning）、評估方法，以及大規模部署 LLM 時所涉及的倫理、社會與法規挑戰。

本書將協助讀者：

- 理解提示工程策略與應用框架
- 深入了解當代 LLM 的架構與能力
- 設計並評估應用於各種真實情境的 LLM 系統
- 思考部署 LLM 時的倫理、安全性與以人為本的面向

對於希望在學術與實務環境中，以審慎且負責任的方式運用 LLM 的讀者而言，本書是一份實用且易於理解的參考資源。

作者簡介

Mohammad Amin Kuhail is an academic and researcher specializing in human-computer interaction and applied artificial intelligence. He earned his Ph.D. in Software Development from the IT University of Copenhagen, Denmark, and holds an M.Sc. in Software Engineering from the University of York, United Kingdom. He is also an IEEE AI Ethics (CertifAIEd) Authorized Lead Assessor, reflecting his expertise in responsible and human-centered AI. Kuhail is currently an Associate Professor at Zayed University's College of Technological Innovation in the United Arab Emirates, where he teaches undergraduate and graduate courses on web technologies, human-computer interaction, and database systems. Alongside teaching, Kuhail leads research on LMMs, chatbot technologies, human-AI interaction, and user behavior. His work examines technical design challenges and the societal implications of AI adoption across domains such as education, healthcare, and digital services. Since joining Zayed University in August 2019, he has contributed to curriculum development, interdisciplinary research, and academic leadership. Previously, he served for 6 years as an Assistant Teaching Professor at the University of Missouri-Kansas City (UMKC), USA, where he received a teaching award in 2018.

Mohammad Tubishat is an Assistant Professor at Zayed University College of Technological Innovation. Tubishat received his Ph.D. in Computer Science (Artificial Intelligence and Natural Language Processing) from the University of Malaya, Malaysia. In addition, he received master's and bachelor's degrees in Computer Science from Yarmouk University, Jordan. Before joining Zayed University, he worked at different universities, including Yarmouk University in Jordan, Taibah University in KSA, Asia Pacific University of Technology and Innovation in Malaysia, and Skyline University College in the UAE. He has been teaching different computer science courses for undergraduate and graduate programs. Tubishat's research areas are Artificial Intelligence, Machine Learning, Sentiment Analysis, Natural Language Processing, and Optimization Algorithms. He has published several peer-reviewed papers in top-ranked journals and conference proceedings.

Sarah Kohail is an Assistant Professor at Zayed University, Abu Dhabi. She holds a Ph.D. in Computer Science from the University of Hamburg and worked as a Research Assistant for several years. She has a deep academic foundation in graph-based text representations, text summarization, and semantic similarity, which she has applied in research and industry. Before joining Zayed University, Kohail spent several years in industry as a Senior Data Scientist, where she developed advanced NLP models, including recommendation systems and knowledge graphs. Her current research focuses on Arabic text detoxification, context-aware anonymization of legal texts, and the application of AI across various domains.

Maha Hadid is an experienced educator with a robust academic background and more than 15 years of teaching experience. Currently a senior instructor at Zayed University in the United Arab Emirates, she holds a Master of Science in Information Sciences and Systems from the University of Marseille, France. Hadid brings extensive expertise to teaching undergraduate IT courses, specializing in system design, database management, and data analysis. In addition to her teaching, she has co-authored a book on object-oriented programming and machine learning. Her professional credentials include being a Fellow of the Higher Education Academy (UK) and a Certified Scrum Master. She has demonstrated exceptional skills in instructional design, developing both blended and classroom-based courses through extensive training in pedagogy and learning theories. Hadid's current focus involves leveraging advanced prompt engineering and low-code system design to develop practical applications across various disciplines.

作者簡介(中文翻譯)

Mohammad Amin Kuhail 是一位專精於人機互動與應用人工智慧的學者及研究人員。他於丹麥 Copenhagen IT University 取得 Software Development 博士學位，並於英國 University of York 取得 Software Engineering 理學碩士學位。此外，他也是 IEEE AI Ethics（CertifAIEd）認證的授權首席評估員，展現其在負責任且以人為本的 AI 領域中的專業。Kuhail 目前任職於阿拉伯聯合大公國 Zayed University 技術創新學院，擔任副教授，教授網頁技術、人機互動與資料庫系統等大學部及研究所課程。除了教學工作之外，Kuhail 也帶領 LMMs（大型多模態模型）、聊天機器人技術、人機互動及使用者行為等領域的研究。他的研究探討技術設計所面臨的挑戰，以及 AI 採用在教育、醫療保健與數位服務等領域所帶來的社會影響。自 2019 年 8 月加入 Zayed University 以來，他一直參與課程開發、跨領域研究及學術領導工作。在此之前，他曾於美國 University of Missouri-Kansas City（UMKC）擔任教學助理教授達 6 年，並於 2018 年獲得教學獎。

Mohammad Tubishat 是 Zayed University 技術創新學院的助理教授。Tubishat 於馬來西亞 University of Malaya 取得 Computer Science 博士學位，研究專長為 Artificial Intelligence 與 Natural Language Processing。此外，他也於約旦 Yarmouk University 取得 Computer Science 碩士及學士學位。在加入 Zayed University 前，他曾任教於多所大學，包括約旦的 Yarmouk University、沙烏地阿拉伯的 Taibah University、馬來西亞的 Asia Pacific University of Technology and Innovation，以及阿拉伯聯合大公國的 Skyline University College。他曾教授大學部及研究所不同的 Computer Science 課程。Tubishat 的研究領域包括 Artificial Intelligence、Machine Learning、Sentiment Analysis、Natural Language Processing，以及 Optimization Algorithms。他已在頂尖期刊及會議論文集發表多篇經同儕審查的論文。

Sarah Kohail 是阿拉伯聯合大公國 Abu Dhabi Zayed University 的助理教授。她於 University of Hamburg 取得 Computer Science 博士學位，並曾擔任研究助理多年。她在以圖為基礎的文字表示法、文字摘要及語意相似度方面具備深厚的學術基礎，並將這些專長應用於研究與產業實務。在加入 Zayed University 前，Kohail 曾在業界擔任資深資料科學家多年，開發包括推薦系統與知識圖譜在內的進階 NLP 模型。她目前的研究重點包括阿拉伯文文字去毒化、法律文本的情境感知匿名化，以及 AI 在各種領域中的應用。

Maha Hadid 是一位經驗豐富的教育工作者，具備扎實的學術背景及超過 15 年的教學經驗。她目前在阿拉伯聯合大公國的 Zayed University 擔任資深講師，並持有法國 Marseille University 的 Information Sciences and Systems 理學碩士學位。Hadid 在大學部 IT 課程教學方面具備廣泛專長，專精於系統設計、資料庫管理及資料分析。除了教學之外，她也共同撰寫了一本關於物件導向程式設計與 Machine Learning 的書籍。她的專業資格包括英國 Higher Education Academy Fellow 及 Certified Scrum Master。透過接受教育學與學習理論方面的廣泛訓練，她在教學設計方面展現卓越能力，能夠開發混成式及課堂式課程。Hadid 目前專注於運用進階提示工程（prompt engineering）與低程式碼系統設計，開發適用於各種學科領域的實務應用。

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