This book explains the principles, structure, and real-world practice of prompt engineering in a way that anyone working with large language models can understand and apply. Rather than treating prompting as a collection of hacks, it walks the reader through how to think about role, context, task, constraints, and examples so that LLMs produce reliable, domain-appropriate outputs. Starting from the basics of “what is a prompt,” the book then connects prompting to how generative AI models actually work, introduces core and advanced prompting patterns (prompt chaining, dynamic templates, tool-augmented prompting), and shows how to evaluate and refine model responses.
Designed for students, educators, and practitioners, each chapter includes learning objectives and hands-on exercises that can be tried directly in tools like ChatGPT. Later chapters move from theory to application, demonstrating how to build LLM-powered chatbots and retrieval-augmented generation (RAG) systems, and how to incorporate ethics, safety, and bias awareness into prompt design. By the end, readers will have a reusable toolkit for crafting effective prompts across education, research, business, and technical use cases.
Table of Contents:
Chapter 1: Introduction to Prompt Engineering Chapter 2: Understanding Generative AI Chapter 3: The Basics of Prompt Design Chapter 4: NLP Foundations for Prompt Engineering Chapter 5: Language and Communication in Prompt Design Chapter 6: Advanced Prompting, Evaluation, and Validation Chapter 7: Ethical and Responsible Prompt Engineering Chapter 8: Building LLM-powered Applications (Chatbots and RAG) Chapter 9: Exploring Prompts with ChatGPT Chapter 10: Future Directions in Prompt Engineering
About the Author :
Akshay Bhuvaneswari Ramakrishnan is a data scientist and researcher specializing in applied artificial intelligence, medical informatics, and explainable machine learning. His work bridges computational modeling and clinical application, with a focus on interpretable frameworks for disease prediction, survival analysis, and multimodal data integration. He has held research positions at Johns Hopkins Medicine and the ACROSS Laboratory for Computational Sustainability, contributing to projects on ICU signal analytics, ocular disease recognition, and ecological modeling. Akshay’s professional experience spans academia, industry, and consulting, including roles as a Visiting Professor in Artificial Intelligence, Machine Learning Engineer at Bezoku LLC, and Data Engineer at Hitachi Energy. Akshay has authored more than 4 refereed journal papers, 9 international book chapters, 2 edited research volumes, 2 authored textbooks, and 4 peer-reviewed conference papers, alongside a patent in the domain of hybrid AI frameworks for healthcare decision support. His publications appear in reputed outlets such as Informatics in Medicine Unlocked, Applied AI Letters, and CRC Press series. As an Intel Software Innovator and Certified MLOps Professional, Akshay integrates high-performance computing tools such as Intel oneAPI to optimize deep learning pipelines. He has conducted over fifty workshops and training programs worldwide, empowering students and professionals in machine learning, MLOps, and prompt engineering. His research vision centers on building transparent, equitable, and computationally efficient AI systems for real-world decision support.
P. Padmakumari is an Assistant Professor of Computer Science and Engineering at School of Computing, SASTRA Deemed University, Thanjavur, Tamilnadu, India. She received her Bachelor’s degree in science from Madurai Kamaraj University, Madurai, Tamil Nadu, India. She obtained her Master’s degree in computer applications from Anna University, Chennai, Tamil Nadu, India. She also received her Master’s degree in computer science and engineering from SASTRA Deemed University, Thanjavur, Tamil Nadu, India. She received her Ph.D. degree from SASTRA Deemed University, Thanjavur, Tamil Nadu, India. She has 16 +years of teaching experience and her research focuses on scientific workflows on heterogeneous distributed systems, Machine learning, AI cloud computing, and fault tolerance. She contributed 20+ papers for many high-quality technology journals in SCI and Scopus that are edited by internationally acclaimed professors and professionals. She is also a reviewer for of several international journals.
R. Manikandan obtained his Ph.D. in VLSI physical design from SASTRA Deemed University, India in 2014. He received his Bachelor of Engineering in computer science from Bharathidasan University in 1996 and Master of Technology in VLSI design from SASTRA Deemed University in 2002. He possesses three decades of academic and 15 years of research experience in the field of computer science and engineering. He has more than 200 research contributions to his credit, which were published in refereed and indexed journals, book chapters and conferences. He has been working as Senior Assistant Professor at SASTRA Deemed University for the last 17 years. He has delivered many lectures and has attended and presented in International Conferences in India and Abroad. He has edited more than 200 research articles, which includes his editorial experience across refereed and indexed journals, conferences and book chapters at national and international levels. His contemporary research interests include big data, data analytics, VLSI design, IoT and health care application.
S. Vidivelli is currently working as an Assistant Professor at the School of Computing, SASTRA University, Thanjavur. Her academic journey includes a Bachelor of Engineering (B.E.) in computer science and engineering from Arasu Engineering College, a Master of Engineering (M.E.) from Anna University, Trichy and a Doctorate from Anna University, Chennai. With over 12 years of teaching experience at renowned institutions, she has actively contributed to curriculum development and academic leadership. Her research expertise lies in the captivating realms of computer vision, machine learning, quantum computing and artificial intelligence. As an academician she has organized seminars, symposium and conferences. She has presented her research findings at national and international conferences. She has also delivered guest lectures in various institutions to share and upgrade her skills. she has published impactful research papers in SCOPUS and Science Indexed journals and book chapters. She is acting as an active reviewer in reputed journals.
S. Magesh is a notable academician in the field of computer science and engineering. He has a distinguished academic career in engineering institutions spanning 17 years and 10 years corporate experience. He has served in various academies of engineering colleges and varsities in different parts of Tamil Nadu. He has published 24 refereed international indexed journals, including Web of Science, ESCI, SCOPUS, Springer, 2 Patents, and 2 books. He is an empanelled trainer at Officer’s Training Academy (OTA-Military Academy), Chennai. He is a visionary, orator, and acclaimed international national trainer. His expertise spans both corporate and academia, and he has been acclaimed for his scholastic achievements. He has won many awards and accolades during his academic career. Presently he serves as the Adjunct Professor, JP Jacobs International University, USA and Empanelled and Visiting Faculty of SRM University.
Harish Garg is working as an Associate Professor at Thapar Institute of Engineering & Technology, Patiala, Punjab, India. He received the Most Outstanding Researcher award in the field of Mathematics from the Career 360 Academy. He is also the recipient of the International Obada-Prize 2022 – Young Distinguished Researchers. He is also the recipient of the Top-Cited paper by an India-based author (2015–2019) from Elsevier. He also serves as an advisory board member of the Universal Scientific Education and Research Network (USERN). He is a Research Fellow of INTI International University, Malaysia. He is ranked in the World’s Top 2% Scientist List published by Stanford University in the consecutive years from 2020–2025. Dr. Garg’s research interests include computational intelligence, multi-criteria decision making, evolutionary algorithms, reliability analysis, expert systems and decision support systems, computing with words and soft computing. He has authored more than 540 papers (over 520 are SCI) published in refereed international journals including IEEE Transactions, Elsevier, Springer, etc. His Google citations are over 30,500 with H-index-94. Dr. Garg also serves on editorial boards of several leading international journals, this includes the Founding Editor-in-Chief of the Journal of Computational and Cognitive Engineering. He is also the Associate Editor of Alexandria Engineering Journal, Journal of Industrial & Management Optimization, CAAI Transactions on Intelligence Technology, etc.
