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.
1. Cover image - p. c 2. Title page - p. i 3. Copyright - p. iv 4. Contents - p. v 5. Contributors - p. xxiii 6. About the Editors - p. xxix 7. Preface - p. xxxi 8. Acknowledgments - p. xxxiii 9. Section I Foundations of large language models - p. 1 10. Chapter 1 Foundations and frameworks for large language models: Concepts and deployment strategies - p. 3 11. Chapter 2 Mathematical foundations and reasoning capabilities of large language models - p. 29 12. Section II Governance, ethics, policy, and law - p. 51 13. Chapter 3 Responsibility gaps in autonomous agentic AI: Legal and ethical blind spots in multiagent and multideveloper systems - p. 53 14. Chapter 4 Business transformation and legal innovation in the age of generative artificial intelligence - p. 73 15. Chapter 5 Policy, law, and artificial intelligence in healthcare: Addressing legal hurdles in the use of large language models - p. 93 16. Chapter 6 Enhancing security and privacy in the integration of large language models within learning management systems - p. 125 17. Section III Healthcare systems & digital health - p. 145 18. Chapter 7 Transforming healthcare with large language models: Innovation, integration, and impact - p. 147 19. Chapter 8 Revolutionizing healthcare systems through large language models - p. 167 20. Chapter 9 SymptoGuide: Revolutionizing digital health through retrieval augmented generation and large language models - p. 187 21. Section IV Mental health, neuroscience & well-being - p. 207 22. Chapter 10 Enhancing mental health and cognitive research with generative artificial intelligence - p. 209 23. Chapter 11 Enhancing mental health and cognitive research with generative AI: Transformative applications, ethical considerations, and future directions - p. 237 24. Chapter 12 Therapeutic large language models in mental health: Evidence, alignment engineering, and SAFEE-based governance - p. 265 25. Chapter 13 Personalized music-based neuro-rehabilitation using generative AI models - p. 299 26. Chapter 14 The role of generative artificial intelligence in shaping the future of mental health research - p. 323 27. Section V Finance, risk & intelligent markets - p. 343 28. Chapter 15 Financial services and risk intelligence powered by large language models - p. 345 29. Chapter 16 Large language model-driven trading: Enhancing financial algorithms with sentiment and risk analysis - p. 365 30. Chapter 17 Leveraging large language models for marketing of financial products for multilingual consumers - p. 383 31. Section VI Marketing, business intelligence & consumer insights - p. 403 32. Chapter 18 Large language model-driven marketing strategy and consumer insights - p. 405 33. Section VII Smart cities, robotics & urban intelligence - p. 423 34. Chapter 19 Leveraging large language models for intelligent urban planning and smart cities - p. 425 35. Chapter 20 Large language models in action: Semantic navigation on TurtleBot4 via Model Context Protocol-based natural language interface - p. 471 36. Index - p. 501
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.
1. Cover image - p. c 2. Title page - p. i 3. Copyright - p. iv 4. Contents - p. v 5. Contributors - p. xxiii 6. About the Editors - p. xxix 7. Preface - p. xxxi 8. Acknowledgments - p. xxxiii 9. Section I Foundations of large language models - p. 1 10. Chapter 1 Foundations and frameworks for large language models: Concepts and deployment strategies - p. 3 11. Chapter 2 Mathematical foundations and reasoning capabilities of large language models - p. 29 12. Section II Governance, ethics, policy, and law - p. 51 13. Chapter 3 Responsibility gaps in autonomous agentic AI: Legal and ethical blind spots in multiagent and multideveloper systems - p. 53 14. Chapter 4 Business transformation and legal innovation in the age of generative artificial intelligence - p. 73 15. Chapter 5 Policy, law, and artificial intelligence in healthcare: Addressing legal hurdles in the use of large language models - p. 93 16. Chapter 6 Enhancing security and privacy in the integration of large language models within learning management systems - p. 125 17. Section III Healthcare systems & digital health - p. 145 18. Chapter 7 Transforming healthcare with large language models: Innovation, integration, and impact - p. 147 19. Chapter 8 Revolutionizing healthcare systems through large language models - p. 167 20. Chapter 9 SymptoGuide: Revolutionizing digital health through retrieval augmented generation and large language models - p. 187 21. Section IV Mental health, neuroscience & well-being - p. 207 22. Chapter 10 Enhancing mental health and cognitive research with generative artificial intelligence - p. 209 23. Chapter 11 Enhancing mental health and cognitive research with generative AI: Transformative applications, ethical considerations, and future directions - p. 237 24. Chapter 12 Therapeutic large language models in mental health: Evidence, alignment engineering, and SAFEE-based governance - p. 265 25. Chapter 13 Personalized music-based neuro-rehabilitation using generative AI models - p. 299 26. Chapter 14 The role of generative artificial intelligence in shaping the future of mental health research - p. 323 27. Section V Finance, risk & intelligent markets - p. 343 28. Chapter 15 Financial services and risk intelligence powered by large language models - p. 345 29. Chapter 16 Large language model-driven trading: Enhancing financial algorithms with sentiment and risk analysis - p. 365 30. Chapter 17 Leveraging large language models for marketing of financial products for multilingual consumers - p. 383 31. Section VI Marketing, business intelligence & consumer insights - p. 403 32. Chapter 18 Large language model-driven marketing strategy and consumer insights - p. 405 33. Section VII Smart cities, robotics & urban intelligence - p. 423 34. Chapter 19 Leveraging large language models for intelligent urban planning and smart cities - p. 425 35. Chapter 20 Large language models in action: Semantic navigation on TurtleBot4 via Model Context Protocol-based natural language interface - p. 471 36. Index - p. 501
