Mastering Prompt Engineering
Deep Insights for Optimizing Large Language Models (LLMs)
- 1st Edition - July 13, 2025
- Latest edition
- Authors: Anand Nayyar, Ajantha Devi Vairamani, Kuldeep Kaswan
- Language: English
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 Langua… Read more
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Description
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.
Key features
Key features
- 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
Readership
Readership
Computer Science researchers, artificial intelligence researchers, and software developers that have immediate and direct responsibilities related to implementing Large Language Models (LLMs) and prompt engineering in their work. The primary audience also includes data scientists, software engineers, as well as researchers and professionals across the fields of science and engineering
Table of contents
Table of contents
1: Basic Insights into Large Language Models
1.1 The Rise of Large Language Models (LLMs) and Generative AI
1.2 Importance of Prompt Engineering for Enhancing LLMs
1.3 History and Background of LLM
2: Foundations of LLM-based Prompt Engineering
2.1 Understanding LLMs: Architecture, Training, and Fine-tuning
2.2 Introduction to Prompt Engineering and its Role in LLMs
2.3 Why Prompt Engineering and its Working?
2.4 Types of Prompts: Conceptual, Contextual, and Conditioning
2.5 Elements of Prompt
2.6 Evaluating Prompt Effectiveness and Quality
3: Familiarity with Prompt Design
3.1 Components of Prompt
3.2 Types of Prompts: Single-Sentence, Multi-Sentence, Query-Based, etc.
3.3 Formatting Guidelines for Effective Prompts
3.4 Selecting Appropriate Prompt Lengths and Granularity
3.5 Handling Special Characters and Symbols in Prompts
4: Pre-processing and Tokenization in Prompt Engineering
4.1 Foundation Concept of Tokenization
4.2 Tokenization Techniques for Different Prompt Types
4.3 Special Tokens and Their Usage in Prompts
4.4 Handling Input Formatting Variations in Prompts
4.5 Multilingual Prompts: Considerations and Techniques
5: State-of-the-Art Techniques in Prompt Engineering
5.1 Cost-effective Techniques for SMEs
5.2 Accessibility: User-friendly Frameworks and Tools
5.3 Community Efforts: Open-source Initiatives and Libraries
5.4 Rule-based Approaches for Prompt Design and Modification
5.5 Template-based Prompts and Language Patterns
5.6 Reinforcement Learning for Automatic Prompt Optimization
5.7 Knowledge Graph Integration for Contextual Prompts
5.8 GAN-based Approaches for Conditioning Prompts
5.9 Comparative Analysis of Prompt Engineering Techniques
6: Diverse Prompt Engineering Models and their Implementations
6.1 Types of Models: BLOOM, GPT 3.5, GPT4, LLAMA, PALM2, LANGCHAIN
6.2 Comparison of Large Language Models
6.3 Advanced Techniques in Prompt Engineering
6.4 Implementing Prompt Engineering
6.5 SMEs and Specific Use Cases
6.6 Ease of Use: Beginner-friendly Interfaces and Tutorials
6.7 Budget Considerations: Cost-effective Models and Free Tier Options
7: Evaluation and Refinement of Prompt Engineering
7.1 Metrics for Evaluating Prompted Generation Quality
7.2 Human Evaluation Methods and User Studies
7.3 Iterative Refinement and Improvising Prompt Quality
8: Prompt Engineering: Ethical Considerations and Challenges
8.1 Introduction to Ethical Considerations in Generative AI
8.2 Bias and Fairness 8.3 Privacy and Security Concerns
8.4 Transparency and Responsibility
8.5 Accountability and Explainability
8.6 SME-specific Risks
8.7 Practical Best Practices 8.8 Transparency and Explainability Tools
9: Case Studies in Prompt Engineering
9.1 Case Study 1: Building and Fine-tuning a Domain-Specific LLM with Prompts
9.2 Case Study 2: Cross-lingual Transfer Learning with Multilingual LLMs and Prompts
9.3 Case Study 3: Controlled Text Generation with Conditional LLMs and Prompts
9.4 Case Study 4: PALM2: Adaptively Large Models for Efficient Training and Inference with Prompts
9.5 Case Study 5: LangChain: Contextual Language Models with External Knowledge and Prompts
9.6 SME Success Stories
9.7 Quantifiable Results 9.8 Challenges and Solutions
10: Future Trends in Large Language Models and Prompt Engineering cum Concluding Remarks
10.1 Advances in LLM Architectures and Training Techniques
10.2 Augmented Prompt Engineering: Human and AI Collaboration
10.3 Explainability and Interpretability in LLM-based Prompt Engineering
10.4 Democratization of Access
10.5 Interdisciplinary Collaborations
10.6 Human-AI Co-creation
10.7 Conclusion and Future Scope
Glossary
References
1.1 The Rise of Large Language Models (LLMs) and Generative AI
1.2 Importance of Prompt Engineering for Enhancing LLMs
1.3 History and Background of LLM
2: Foundations of LLM-based Prompt Engineering
2.1 Understanding LLMs: Architecture, Training, and Fine-tuning
2.2 Introduction to Prompt Engineering and its Role in LLMs
2.3 Why Prompt Engineering and its Working?
2.4 Types of Prompts: Conceptual, Contextual, and Conditioning
2.5 Elements of Prompt
2.6 Evaluating Prompt Effectiveness and Quality
3: Familiarity with Prompt Design
3.1 Components of Prompt
3.2 Types of Prompts: Single-Sentence, Multi-Sentence, Query-Based, etc.
3.3 Formatting Guidelines for Effective Prompts
3.4 Selecting Appropriate Prompt Lengths and Granularity
3.5 Handling Special Characters and Symbols in Prompts
4: Pre-processing and Tokenization in Prompt Engineering
4.1 Foundation Concept of Tokenization
4.2 Tokenization Techniques for Different Prompt Types
4.3 Special Tokens and Their Usage in Prompts
4.4 Handling Input Formatting Variations in Prompts
4.5 Multilingual Prompts: Considerations and Techniques
5: State-of-the-Art Techniques in Prompt Engineering
5.1 Cost-effective Techniques for SMEs
5.2 Accessibility: User-friendly Frameworks and Tools
5.3 Community Efforts: Open-source Initiatives and Libraries
5.4 Rule-based Approaches for Prompt Design and Modification
5.5 Template-based Prompts and Language Patterns
5.6 Reinforcement Learning for Automatic Prompt Optimization
5.7 Knowledge Graph Integration for Contextual Prompts
5.8 GAN-based Approaches for Conditioning Prompts
5.9 Comparative Analysis of Prompt Engineering Techniques
6: Diverse Prompt Engineering Models and their Implementations
6.1 Types of Models: BLOOM, GPT 3.5, GPT4, LLAMA, PALM2, LANGCHAIN
6.2 Comparison of Large Language Models
6.3 Advanced Techniques in Prompt Engineering
6.4 Implementing Prompt Engineering
6.5 SMEs and Specific Use Cases
6.6 Ease of Use: Beginner-friendly Interfaces and Tutorials
6.7 Budget Considerations: Cost-effective Models and Free Tier Options
7: Evaluation and Refinement of Prompt Engineering
7.1 Metrics for Evaluating Prompted Generation Quality
7.2 Human Evaluation Methods and User Studies
7.3 Iterative Refinement and Improvising Prompt Quality
8: Prompt Engineering: Ethical Considerations and Challenges
8.1 Introduction to Ethical Considerations in Generative AI
8.2 Bias and Fairness 8.3 Privacy and Security Concerns
8.4 Transparency and Responsibility
8.5 Accountability and Explainability
8.6 SME-specific Risks
8.7 Practical Best Practices 8.8 Transparency and Explainability Tools
9: Case Studies in Prompt Engineering
9.1 Case Study 1: Building and Fine-tuning a Domain-Specific LLM with Prompts
9.2 Case Study 2: Cross-lingual Transfer Learning with Multilingual LLMs and Prompts
9.3 Case Study 3: Controlled Text Generation with Conditional LLMs and Prompts
9.4 Case Study 4: PALM2: Adaptively Large Models for Efficient Training and Inference with Prompts
9.5 Case Study 5: LangChain: Contextual Language Models with External Knowledge and Prompts
9.6 SME Success Stories
9.7 Quantifiable Results 9.8 Challenges and Solutions
10: Future Trends in Large Language Models and Prompt Engineering cum Concluding Remarks
10.1 Advances in LLM Architectures and Training Techniques
10.2 Augmented Prompt Engineering: Human and AI Collaboration
10.3 Explainability and Interpretability in LLM-based Prompt Engineering
10.4 Democratization of Access
10.5 Interdisciplinary Collaborations
10.6 Human-AI Co-creation
10.7 Conclusion and Future Scope
Glossary
References
Product details
Product details
- Edition: 1
- Latest edition
- Published: July 13, 2025
- Language: English
About the authors
About the authors
AN
Anand Nayyar
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, Vietnam. He has published numerous research papers in various high-impact journals and holds 10 Australian patents and 1 Indian Design to his credit in the area of Wireless Communications, Artificial Intelligence, IoT and Image Processing.
Affiliations and expertise
Professor, Scientist, Vice-Chairman (Research) and Director at IoT and Intelligent Systems Lab, Duy Tan University, VietnamAV
Ajantha Devi Vairamani
Dr. Ajantha Devi Vairamani is a distinguished Research Head at AP3 Solutions in Chennai, India and is a prominent figure in computer science and artificial intelligence. With a PhD from the University of Madras in 2015, she has played pivotal roles in UGC Major Research Projects and holds prestigious certifications from Microsoft Corp. Her academic prowess is evident in over 50 published papers and numerous books in computer science. Actively participating in international conferences, she contributes to research collaboration in various roles. Dr. Ajantha Devi's groundbreaking work in artificial intelligence, machine learning, and deep learning has led to Australian Patents. Her research spans Image Processing, Signal Processing, Pattern Matching, and Natural Language Processing, addressing real-world challenges. Her dedication has earned her Best Paper Presentation Awards and international honors, solidifying her position as a leading figure in the field, influencing both academia and industry.
Affiliations and expertise
Research Head, AP3 Solutions, IndiaKK
Kuldeep Kaswan
Dr. Kuldeep Singh Kaswan is a distinguished academic figure, currently affiliated with the School of Computing Science & Engineering at Galgotias University in Uttar Pradesh, India. His extensive contributions are centered around the fields of Brain-Computer Interface (BCI), Cyborg technology, and Data Science. With a remarkable academic journey spanning thirteen years and experience garnered from esteemed global institutions such as Amity University, Noida, Gautam Buddha University in Greater Noida, and PDM University in Bahadurgarh, he has solidified his position as a leading expert in his domain. Dr. Kaswan holds a Doctorate in Computer Science from Banasthali Vidyapith in Rajasthan, a testament to his dedication to advancing knowledge in his field. He has also been awarded the prestigious Doctor of Engineering (D. Engg.) degree from the Dana Brain Health Institute in Iran, further enhancing his international recognition and expertise. His academic journey includes a Master's Degree in Computer Science and Engineering from Choudhary Devi Lal University in Sirsa, Haryana. A true mentor and guide, Dr. Kaswan has played a pivotal role in supervising numerous undergraduate and postgraduate projects for engineering students. He has authored nine books and contributed more than 50 book chapters, both at the national and international levels. His extensive body of work also includes numerous publications in esteemed international and national journals, as well as presentations at conferences.
Affiliations and expertise
School of Computing Science and Engineering, Galgotias University, IndiaView book on ScienceDirect
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