Llms in Practice

Real World Applications, Challenges and Success Stories
Erscheinungsjahr 2026
ISBN: 978-0-443-44344-2
Verlag: Elsevier Science

Buch, Englisch, 400 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 449 g

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. LLMs in Practice: Real World Applications, Challenges & Success Stories addresses a significant gap in current literature by offering a focused and practice-oriented examination of how Large Language Models (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.

Autoren/Hrsg.

Singh, Kiran Jot

Mahajan, Shubham

Kapoor, Divneet Singh

Thakur, Khushal

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Natürliche Sprachen & Maschinelle Übersetzung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse

Inhaltsverzeichnis

Section I: Foundations of Large Language Models
1. Foundations and Frameworks for Large Language Models: Concepts and Deployment Strategies
2. Mathematical Foundations and Reasoning Capabilities of Large Language Models

Section II: Governance, Ethics, Policy, and Law
3. Responsibility Gaps in Autonomous Agentic AI: Legal and Ethical Blind Spots in Multi-Agent and Multi-Developer Systems
4. Business Transformation and Legal Innovation in the Age of Generative AI
5. Policy, Law, and AI in Healthcare: Addressing Legal Hurdles in the Use of Large Language Models
6. Enhancing Security and Privacy in the Integration of Large Language Models within Learning Management Systems

Section III: Healthcare Systems & Digital Health
7. Transforming Healthcare with Large Language Models: Innovation, Integration, and Impact
8. Revolutionizing Healthcare Systems Through Large Language Models
9. SymptoGuide: Revolutionising Digital Health through Retrieval- Augmented Generation and LLMs

Section IV: Mental Health, Neuroscience & Well-Being
10. Enhancing Mental Health and Cognitive Research with Generative AI
11. Enhancing Mental Health and Cognitive Research with Generative AI: Transformative Applications, Ethical Considerations, and Future Directions
12. Therapeutic LLMs in Mental Health: Evidence, Alignment Engineering, and SAFEE-Based Governance
13. Personalized Music-Based Neuro-Rehabilitation Using Generative AI Models
14. The Role of Generative AI in Shaping the Future of Mental Health Research

Section V: Finance, Risk & Intelligent Markets
15. Financial Services and Risk Intelligence Powered by LLMs
16. LLM-Driven Trading: Enhancing Financial Algorithms with Sentiment and Risk Analysis
17. Leveraging LLMs for marketing of Financial products for multi-lingual Consumers

Section VI: Marketing, Business Intelligence & Consumer Insights
18. LLM-Driven Marketing Strategy & Consumer Insights

Section VII: Smart Cities, Robotics & Urban Intelligence
19. Leveraging Large Language Models for Intelligent Urban Planning and Smart Cities
20. LLMs in Action: Semantic Navigation on TurtleBot4 via MCP-Based Natural Language Interface

Über Autor(innen)

Mahajan, Shubham
Dr. Shubham Mahajan is an academic and researcher, member of IEEE, ACM, and IAENG. He earned a B.Tech from Baba Ghulam Shah Badshah University, an M.Tech from Chandigarh University, and a PhD from Shri Mata Vaishno Devi University. He is currently Assistant Professor at Amity University, Haryana. His research spans artificial intelligence and image processing, including video compression, image segmentation, fuzzy entropy, nature-inspired optimization, data mining, machine learning, robotics, and optical communications. He holds patents internationally and has published widely in high-impact venues; he has edited several Scopus-indexed books. He has received multiple awards for research excellence and travel support from IEEE, among others. He has served as IEEE Campus Ambassador at premier institutes and promotes international collaborations. He participates in technical program committees and editorial boards for conferences and journals, shaping discourse in AI and image processing.

Singh, Kiran Jot
Kiran Jot Singh is currently an Associate Professor and Associate Dean (Academic Affairs) at Chandigarh University, Mohali, India. He has done PhD in the domain of Human Robot Interaction. He has also filed 12 patents with the Indian patent office. He is working on various funded projects, and his research interests include human robot/computer interaction, bio feedback and happiness studies through technology.

Thakur, Khushal
Dr. Khushal Thakur is Associate Dean – Academic Affairs and Associate Professor at Chandigarh University with over a decade of experience in Electronics & Communication Engineering. He holds a Ph.D. in Massive MIMO systems and has published extensively in reputed journals such as IEEE Access, Springer, and CRC Press. His research spans IoT, embedded systems, wireless networks, and AI, and he holds several Indian patents, including three granted ones. Dr. Thakur has contributed significantly to academic planning, curriculum design, and quality assurance, supporting initiatives aligned with NBA, NAAC, and ABET standards.

Kapoor, Divneet Singh
Divneet Singh Kapoor is currently an Associate Professor and Associate Dean (Academic Affairs) at Chandigarh University, Mohali, India. He has done PhD in the domain of signal processing and wireless communication. He has filed multiple patents with the Indian patent office and has been granted several, showcasing his innovative contributions to the field. His research interests span embedded systems, Internet of Things (IoT), robotics, and affective computing.