Harish Garg

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.

This book explains the foundational principles of fuzzy algebra, including fuzzy sets, fuzzy operations, and fuzzy relations. It shows how fuzzy algebra can be applied in artificial intelligence, particularly in areas such as decision-making, pattern recognition, and machine learning, where uncertainty and imprecision are common. It demonstrates the use of fuzzy algebra in engineering disciplines, such as control systems, cybersecurity, signal processing, and robotics, where fuzzy logic can be used to model and solve complex and real-world problems. This makes the book valuable not just for process engineers, but also for researchers in multiple domains. This book is designed to familiarize data science, machine learning and other engineering professionals, in a relatively simple and easy to understand fashion, with decision making skills founded on calculations and case study based quantitative analysis.

This book offers a comprehensive review of the different types of fuzzy sets that have been developed in the last few years. Each chapter is dedicated to a type of fuzzy set, which is at first introduced and described in detail, together with its properties. In turn, its application in an optimization and/or decision-making problem is discussed. In particular, the book highlights current research gaps and future research directions on fuzzy numbers in decision analysis and transportation problems. It is intended as a self-contained reference guide for scientists and engineers interested in applying fuzzy set theory for solving optimization problems.