Mastering Prompt Engineering: Deep Insights for Optimizing Large Language Models (LLMs) is a comprehensive guide that takes readers on a journey through the world of Large Language Models (LLMs) and prompt engineering. Covering foundational concepts, advanced techniques, ethical considerations, and real-world case studies, this book equips both novices and experts to navigate the complex LLM landscape. It provides insights into LLM architecture, training, and prompt engineering methods, while addressing ethical concerns such as bias and privacy. Real-world case studies showcase the practical…mehr
Mastering Prompt Engineering: Deep Insights for Optimizing Large Language Models (LLMs) is a comprehensive guide that takes readers on a journey through the world of Large Language Models (LLMs) and prompt engineering. Covering foundational concepts, advanced techniques, ethical considerations, and real-world case studies, this book equips both novices and experts to navigate the complex LLM landscape. It provides insights into LLM architecture, training, and prompt engineering methods, while addressing ethical concerns such as bias and privacy. Real-world case studies showcase the practical application of prompt engineering in a wide range of settings. This resource is not just for specialists but is a practical and ethically conscious guide for AI practitioners, students, scientific researchers, and anyone interested in harnessing the potential of LLMs in natural language processing and generation. Mastering Prompt Engineering serves as a gateway to a deeper understanding of LLMs and their responsible and effective utilization through its comprehensive, ethical, and practical approach.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Dr. Anand Nayyar received his Ph.D (Computer Science) from Desh Bhagat University in 2017 in Wireless Sensor Networks and Swarm Intelligence. He is currently working in Graduate School, Faculty of Information Technology- Duy Tan University, Da Nang, Vietnam. He has published 100+ research papers in various high-impact journals. He has authored, co-authored, and edited 30+ books. He has 10 Australian patents and 1 Indian Design to his credit in the area of Wireless Communications, Artificial Intelligence, IoT and Image Processing. Awarded 30+ Awards for Teaching and Research, including Young Scientist, Best Scientist, Young Researcher Award, Outstanding Researcher Award, Excellence in Teaching. He is acting as Associate Editor for Wireless Networks (Springer), Computer Communications (Elsevier), IET-Quantum Communications, IET Wireless Sensor Systems, IET Networks, IJDST, IJISP, IJCINI. He is acting as Editor-in-Chief of IGI-Global, USA Journal titled "International Journal of Smart Vehicles and Smart Transportation (IJSVST)?.
Inhaltsangabe
1. Basic Insights into Large Language Models 2. Foundations of LLM-based Prompt Engineering 3. Familiarity with Prompt Design 4. Pre-processing and Tokenization in Prompt Engineering 5. State-of-the-Art Techniques in Prompt Engineering 6. Diverse Prompt Engineering Models and their Implementations 7. Evaluation and Refinement of Prompt Engineering 8. Prompt Engineering: Ethical Considerations and Challenges 9. Case Studies in Prompt Engineering 10. Future Trends in Large Language Models and Prompt Engineering cum Concluding Remarks
1. Basic Insights into Large Language Models 2. Foundations of LLM-based Prompt Engineering 3. Familiarity with Prompt Design 4. Pre-processing and Tokenization in Prompt Engineering 5. State-of-the-Art Techniques in Prompt Engineering 6. Diverse Prompt Engineering Models and their Implementations 7. Evaluation and Refinement of Prompt Engineering 8. Prompt Engineering: Ethical Considerations and Challenges 9. Case Studies in Prompt Engineering 10. Future Trends in Large Language Models and Prompt Engineering cum Concluding Remarks
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