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. 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
Section I: Foundations of Large Language Models1. Foundations and Frameworks for Large Language Models: Concepts and Deployment Strategies2. Mathematical Foundations and Reasoning Capabilities of Large Language ModelsSection II: Governance, Ethics, Policy, and Law3. Responsibility Gaps in Autonomous Agentic AI: Legal and Ethical Blind Spots in Multi-Agent and Multi-Developer Systems4. Business Transformation and Legal Innovation in the Age of Generative AI5. Policy, Law, and AI in Healthcare: Addressing Legal Hurdles in the Use of Large Language Models6. Enhancing Security and Privacy in the Integration of Large Language Models within Learning Management SystemsSection III: Healthcare Systems & Digital Health7. Transforming Healthcare with Large Language Models: Innovation, Integration, and Impact8. Revolutionizing Healthcare Systems Through Large Language Models9. SymptoGuide: Revolutionising Digital Health through Retrieval- Augmented Generation and LLMsSection IV: Mental Health, Neuroscience & Well-Being10. Enhancing Mental Health and Cognitive Research with Generative AI11. Enhancing Mental Health and Cognitive Research with Generative AI: Transformative Applications, Ethical Considerations, and Future Directions12. Therapeutic LLMs in Mental Health: Evidence, Alignment Engineering, and SAFEE-Based Governance13. Personalized Music-Based Neuro-Rehabilitation Using Generative AI Models14. The Role of Generative AI in Shaping the Future of Mental Health ResearchSection V: Finance, Risk & Intelligent Markets15. Financial Services and Risk Intelligence Powered by LLMs16. LLM-Driven Trading: Enhancing Financial Algorithms with Sentiment and Risk Analysis17. Leveraging LLMs for marketing of Financial products for multi-lingual ConsumersSection VI: Marketing, Business Intelligence & Consumer Insights18. LLM-Driven Marketing Strategy & Consumer InsightsSection VII: Smart Cities, Robotics & Urban Intelligence19. Leveraging Large Language Models for Intelligent Urban Planning and Smart Cities20. LLMs in Action: Semantic Navigation on TurtleBot4 via MCP-Based Natural Language Interface
