LLMs in Practice

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LLMs in Practice

Real World Applications, Challenges and Success Stories

Artificial intelligence Natural language and machine translation

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Language: English

Published by: Elsevier

Published on: 24th June 2026

Format: LCP-protected ePub

ISBN: 9780443443459


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.

Key Topics Covered

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

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