4 Importance of Prompt Engineering in Generative AI Models
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M. Abinaya
Abstract
For the model effectiveness in generative artificial intelligence, prompt engineering and design play a very important role. For the influence of the model and the behavior prompt engineering the subdomain of machine learning and natural language processing (NLP) plays an important role in determining the model’s output. Robustness, performance interpretability importance, and the way to improve are discussed in this chapter. The first section of the book deals with the techniques, principles, and ideas discussed. Relevance, inventiveness, and coherence are the inputs essentially needed for the function. To meet the tasks and the goals the relationship between the prompt design and the capabilities and the complex relationship are discussed. The chapter also specifies various techniques for the prompt creation and restriction of linguistics methods using templates and specific domain advice. How the model interoperability and the mitigation of bias in prompt engineering are examined is shown in this chapter. Transparency and recognizing the bias in the AI system are also covered in this chapter. In the field of text generation, image synthesis, and conversational agents’ real time and case studies are discussed in this chapter. The challenges and future directions of prompt engineering are discussed in this chapter.
Abstract
For the model effectiveness in generative artificial intelligence, prompt engineering and design play a very important role. For the influence of the model and the behavior prompt engineering the subdomain of machine learning and natural language processing (NLP) plays an important role in determining the model’s output. Robustness, performance interpretability importance, and the way to improve are discussed in this chapter. The first section of the book deals with the techniques, principles, and ideas discussed. Relevance, inventiveness, and coherence are the inputs essentially needed for the function. To meet the tasks and the goals the relationship between the prompt design and the capabilities and the complex relationship are discussed. The chapter also specifies various techniques for the prompt creation and restriction of linguistics methods using templates and specific domain advice. How the model interoperability and the mitigation of bias in prompt engineering are examined is shown in this chapter. Transparency and recognizing the bias in the AI system are also covered in this chapter. In the field of text generation, image synthesis, and conversational agents’ real time and case studies are discussed in this chapter. The challenges and future directions of prompt engineering are discussed in this chapter.
Kapitel in diesem Buch
- Frontmatter I
- Preface V
- Contents VII
- About the Editors IX
- List of Contributors XI
- 1 Unveiling the Power of Generative AI: A Journey into Large Language Models 1
- 2 Early Roots of Generative AI Models and LLM: A Diverse Landscape 23
- 3 Generative AI Models and LLM: Training Techniques and Evaluation Metrics 43
- 4 Importance of Prompt Engineering in Generative AI Models 69
- 5 LLM Pretraining Methods 93
- 6 LLM Fine-Tuning: Instruction and Parameter-Efficient Fine-Tuning (PEFT) 117
- 7 Reinforcement Learning from Human Feedback (RLHF) 135
- 8 Exploring the Applications on Generative AI and LLM 155
- 9 Bias and Fairness in Generative AI 177
- 10 Future Directions and Open Problems in Generative AI 193
- 11 Optimizing Sustainable Project Management Life Cycle Using Generative AI Modeling 213
- 12 Generative AI and LLM: Case Study in Finance 231
- 13 Generative AI and LLM: Case Study in E-Commerce 253
- Index 273
Kapitel in diesem Buch
- Frontmatter I
- Preface V
- Contents VII
- About the Editors IX
- List of Contributors XI
- 1 Unveiling the Power of Generative AI: A Journey into Large Language Models 1
- 2 Early Roots of Generative AI Models and LLM: A Diverse Landscape 23
- 3 Generative AI Models and LLM: Training Techniques and Evaluation Metrics 43
- 4 Importance of Prompt Engineering in Generative AI Models 69
- 5 LLM Pretraining Methods 93
- 6 LLM Fine-Tuning: Instruction and Parameter-Efficient Fine-Tuning (PEFT) 117
- 7 Reinforcement Learning from Human Feedback (RLHF) 135
- 8 Exploring the Applications on Generative AI and LLM 155
- 9 Bias and Fairness in Generative AI 177
- 10 Future Directions and Open Problems in Generative AI 193
- 11 Optimizing Sustainable Project Management Life Cycle Using Generative AI Modeling 213
- 12 Generative AI and LLM: Case Study in Finance 231
- 13 Generative AI and LLM: Case Study in E-Commerce 253
- Index 273
