LLMs in Practice
Real World Applications, Challenges and Success Stories
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A termék adatai:
- Kiadó Elsevier Science
- Megjelenés dátuma 2026. augusztus 1.
- ISBN 9780443443442
- Kötéstípus Puhakötés
- Terjedelem400 oldal
- Méret 235x191 mm
- Súly 450 g
- Nyelv angol 700
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Hosszú leírás:
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.
TöbbTartalomjegyzék:
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
