Mastering Prompt Engineering
Deep Insights for Optimizing Large Language Models (LLMs)
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71 261 Ft (67 867 Ft + 5% VAT)
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71 261 Ft
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Product details:
- Publisher Morgan Kaufmann
- Date of Publication 1 August 2025
- ISBN 9780443339042
- Binding Paperback
- No. of pages250 pages
- Size 234x190 mm
- Weight 450 g
- Language English 700
Categories
Long description:
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.
MoreTable of Contents:
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
Mastering Prompt Engineering: Deep Insights for Optimizing Large Language Models (LLMs)
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