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Full Description
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.
Contents
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
