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Mastering Prompt Engineering: Deep Insights for Optimizing Large Language Models (LLMs)

Autor Anand Nayyar, Ajantha Devi Vairamani, Kuldeep Kaswan
en Limba Engleză Paperback – aug 2025
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.

  • Addresses ethical concerns and provides strategies for mitigating bias and ensuring responsible AI practices
  • Covers foundational concepts, advanced techniques, and the broader landscape of LLMs, equipping readers with a well-rounded understanding
  • Serves as a gateway to a deeper understanding of LLMs and their responsible and effective utilization
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Specificații

ISBN-13: 9780443339042
ISBN-10: 044333904X
Pagini: 250
Dimensiuni: 191 x 235 mm
Editura: ELSEVIER SCIENCE

Cuprins

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