Optimizing and Managing Large Language Models: Foundations, Applications, and Future Directions provides a comprehensive exploration of large language models (LLMs), their architectures, optimization techniques, and real-world applications. The book examines transformer models, retrieval-augmented generation, multimodal learning, and scalability challenges, along with solutions such as model compression and federated learning.It highlights interdisciplinary applications in healthcare, software engineering, and scientific research while addressing ethical concerns like bias, fairness, and privacy. Covering both theoretical foundations and practical implementations, this volume serves as an essential resource for researchers, professionals, and students, offering insights into the future of intelligent, autonomous AI systems.

Optimizing and Managing Large Language Models: Foundations, Applications, and Future Directions provides a comprehensive exploration of large language models (LLMs), their architectures, optimization techniques, and real-world applications. The book examines transformer models, retrieval-augmented generation, multimodal learning, and scalability challenges, along with solutions such as model compression and federated learning.It highlights interdisciplinary applications in healthcare, software engineering, and scientific research while addressing ethical concerns like bias, fairness, and privacy. Covering both theoretical foundations and practical implementations, this volume serves as an essential resource for researchers, professionals, and students, offering insights into the future of intelligent, autonomous AI systems.

Optimizing and Managing Large Language Models: Foundations, Applications, and Future Directions provides a comprehensive exploration of large language models (LLMs), their architectures, optimization techniques, and real-world applications. The book examines transformer models, retrieval-augmented generation, multimodal learning, and scalability challenges, along with solutions such as model compression and federated learning.It highlights interdisciplinary applications in healthcare, software engineering, and scientific research while addressing ethical concerns like bias, fairness, and privacy. Covering both theoretical foundations and practical implementations, this volume serves as an essential resource for researchers, professionals, and students, offering insights into the future of intelligent, autonomous AI systems.