LLMs in Practice

Real World Applications, Challenges and Success Stories

Kiran Jot Singh, Shubham Mahajan, Divneet Singh Kapoor, Khushal Thakur

• Publisher: Elsevier
• Year: 2026
• Topic: Computer Science
• eBook ISBN:
  9780443443459
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

LLMs in Practice: Real World Applications, Challenges and Success Stories offers a deeply applied, interdisciplinary perspective on how Large Language Models (LLMs) are being integrated into the real world—spanning industries, healthcare, education, governance, mental health, creative domains, and intelligent systems. The book presents a blend of technical insights, sector-specific applications, governance frameworks, and ethical considerations. Designed for both academic and professional audiences, it equips readers to responsibly deploy LLMs while fostering innovation, equity, and scalability. The book addresses a significant gap in current literature by offering a focused, practice-oriented examination on how LLMs are being applied across diverse real-world domains.While there is widespread academic and public interest in generative AI, there exists no single resource that cohesively captures its deployment frameworks, sector-specific applications, ethical considerations, and pedagogical integration—especially from a multidisciplinary and global perspective. This book provides deployment guidance, prompt optimization, and reliability strategies; governance frameworks, risk mitigation tools, and audit strategies; and offers case studies, instructional models, project templates, career-aligned examples, and skill-building paths. - Provides a comprehensive understanding of how LLMs are transforming sectors such as healthcare, education, law, and business - Serves as a reference for researchers, practitioners, and innovators seeking to design, evaluate, and scale generative AI systems - Supports educators and students by offering structured resources for teaching, learning, and project-based engagement with LLMs - Promotes responsible innovation by highlighting frameworks for ethical governance, transparency, and inclusive AI adoption across varied socioeconomic contexts

Table of contents

• Cover image
• Title page
• Copyright
• Contents
• Contributors
• About the Editors
• Preface
• Acknowledgments
• Section I Foundations of large language models
• Section II Governance, ethics, policy, and law
• Section III Healthcare systems & digital health
• Section IV Mental health, neuroscience & well-being
• Section V Finance, risk & intelligent markets
• Section VI Marketing, business intelligence & consumer insights
• Section VII Smart cities, robotics & urban intelligence
• Index