{"product_id":"textual-intelligence-large-language-models-and-their-real-world-applications-hardback-9781394287468","title":"Textual Intelligence; Large Language Models and Their Real-World Applications (Hardback) 9781394287468","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eTextual Intelligence\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eLarge Language Models and Their Real-World Applications\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eMeenakshi Malik (Edited by), Preeti Sharma (Edited by), Susheela Hooda (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394287468, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 5 August 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e528 pages\u003cbr\u003e28 x 19 x 2.6 cm, 0.925 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eThe book is a must-have resource for anyone looking to understand the complexities of generative AI, offering comprehensive insights into LLMs, effective training strategies, and practical applications.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eTextual Intelligence: Large Language Models and Their Real-World Applications\u003c\/i\u003e provides an overview of generative AI and its multifaceted applications, as well as the significance and potential of Large Language Models (LLMs), including GPT and LLaMA. It addresses the generative AI project lifecycle, challenges in existing data architectures, proposed use case planning and scope definition, model deployment, and application integration. Training LLMs, data requirements for effective LLM training, pre-training and fine-tuning processes, and navigating computational resources and infrastructure are also discussed. The volume delves into in-context learning and prompt engineering, offering strategies for crafting effective prompts, techniques for controlling model behavior and output quality, and best practices for prompt engineering. \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eTextual Intelligence: Large Language Models and Their Real-World Applications\u003c\/i\u003e also discusses cost optimization strategies for LLM training, aligning models to human values, optimizing model architectures, the power of transfer learning and fine-tuning, instruction fine-tuning for precision, and parameter-efficient fine-tuning (PEFT) with adapters such as LoRA, QLoRA, and soft prompts, making it an essential guide for both beginners and industry veterans. \u003c\/p\u003e\n\u003cp\u003eReaders will find this book: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eExplores the real-world potential of large language models;\u003c\/li\u003e \u003cli\u003eIntroduces industry-changing AI solutions;\u003c\/li\u003e \u003cli\u003eProvides advanced insights on AI and its models.\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003cb\u003eAudience\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eIndustry professionals, academics, graduate students, and researchers seeking real-world solutions using generative AI.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1: Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction: Overview of Generative AI and Multifaceted Applications, Significance, and Potential of LLMs 3\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eK. Mukheja, S. Mittal, C. Monga and S. Annam\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction to Generative AI and LLM 4\u003c\/p\u003e \u003cp\u003e1.2 Applications of Generative AI 6\u003c\/p\u003e \u003cp\u003e1.2.1 Medical 6\u003c\/p\u003e \u003cp\u003e1.2.2 Education 7\u003c\/p\u003e \u003cp\u003e1.2.3 Finance 7\u003c\/p\u003e \u003cp\u003e1.3 Detail Case Study—Rise of Chatbots 9\u003c\/p\u003e \u003cp\u003e1.3.1 Empowering Chatbots with Large Language Models 10\u003c\/p\u003e \u003cp\u003e1.3.2 Chatbots in Medical and Healthcare Education 10\u003c\/p\u003e \u003cp\u003e1.3.3 Chatbots in Finance 11\u003c\/p\u003e \u003cp\u003e1.3.4 Chatbots in Tourism 11\u003c\/p\u003e \u003cp\u003e1.4 Examples 12\u003c\/p\u003e \u003cp\u003e1.5 Comparative Analysis of Generative AI Techniques 14\u003c\/p\u003e \u003cp\u003e1.6 Future Scope and Potential 16\u003c\/p\u003e \u003cp\u003e1.7 Conclusion 17\u003c\/p\u003e \u003cp\u003eReferences 17\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 A Comprehensive Study of Large Language Models 21\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePawan Kumar, Anu Chaudhary, Shashank Sahu, Mradul Kumar Jain and Updesh Kumar Jaiswal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 22\u003c\/p\u003e \u003cp\u003e2.2 Background 24\u003c\/p\u003e \u003cp\u003e2.2.1 Tokenization 24\u003c\/p\u003e \u003cp\u003e2.2.2 Positions Encoding 24\u003c\/p\u003e \u003cp\u003e2.2.3 Attention in LLM 25\u003c\/p\u003e \u003cp\u003e2.2.4 Activation Function 26\u003c\/p\u003e \u003cp\u003e2.2.5 Data Preprocessing 26\u003c\/p\u003e \u003cp\u003e2.2.6 Architecture Model 27\u003c\/p\u003e \u003cp\u003e2.2.7 Pre-Training 28\u003c\/p\u003e \u003cp\u003e2.2.8 Fine-Tuning 29\u003c\/p\u003e \u003cp\u003e2.3 Large Language Models (LLMs) 31\u003c\/p\u003e \u003cp\u003e2.3.1 BERT (Bidirectional Encoder Representations Transformer) 31\u003c\/p\u003e \u003cp\u003e2.3.1.1 BERT Architecture 31\u003c\/p\u003e \u003cp\u003e2.3.1.2 Working of BERT Model 32\u003c\/p\u003e \u003cp\u003e2.3.1.3 Fine-Tuning in BERT 33\u003c\/p\u003e \u003cp\u003e2.3.1.4 BERT Applications 34\u003c\/p\u003e \u003cp\u003e2.3.1.5 Advantages of the BERT Language Model 35\u003c\/p\u003e \u003cp\u003e2.3.1.6 Disadvantages of the BERT Language Model 35\u003c\/p\u003e \u003cp\u003e2.3.2 ChatGPT (Chat Generative Pre-Trained Transformer) 36\u003c\/p\u003e \u003cp\u003e2.3.2.1 ChatGPT Architecture 36\u003c\/p\u003e \u003cp\u003e2.3.2.2 Tokenization 38\u003c\/p\u003e \u003cp\u003e2.3.2.3 Embeddings in ChatGPT 39\u003c\/p\u003e \u003cp\u003e2.3.2.4 Pre-Training 39\u003c\/p\u003e \u003cp\u003e2.3.2.5 Fine-Tuning 39\u003c\/p\u003e \u003cp\u003e2.4 Challenges and Future Directions 40\u003c\/p\u003e \u003cp\u003e2.5 Conclusion 40\u003c\/p\u003e \u003cp\u003eReferences 41\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2: Generative AI Project Lifecycle 45\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 A Deep Learning Methodology with Transformers LLM to Calculate the Global Temperature Difference in Recent Years 47\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAna Carolina Borges Monteiro, Reinaldo Padilha França and Rodrigo Bonacin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 48\u003c\/p\u003e \u003cp\u003e3.2 Overview of Literature IoT 50\u003c\/p\u003e \u003cp\u003e3.3 Overview of Literature AI 53\u003c\/p\u003e \u003cp\u003e3.4 Methodology 56\u003c\/p\u003e \u003cp\u003e3.5 Results 57\u003c\/p\u003e \u003cp\u003e3.6 Discussion 61\u003c\/p\u003e \u003cp\u003e3.7 Conclusions 63\u003c\/p\u003e \u003cp\u003eReferences 64\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Navigating the Generative AI Project Ecosystem with a Focus on Addressing Data Architecture Complexities and Strategic Model Selection for Optimal Outcomes 67\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMohammad Shabaz, Shanky Goyal, Ismail Keshta, Mukesh Soni and Vijay Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 68\u003c\/p\u003e \u003cp\u003e4.2 Literature Review 69\u003c\/p\u003e \u003cp\u003e4.3 Proposed Method 72\u003c\/p\u003e \u003cp\u003e4.4 Result 83\u003c\/p\u003e \u003cp\u003e4.5 Conclusion 88\u003c\/p\u003e \u003cp\u003eReferences 89\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Generative AI Project Life Cycle—Use Case Planning and Scope Definition 93\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJyoti Rani, Pawan Kumar and Nidhi Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 What is Generative AI? 94\u003c\/p\u003e \u003cp\u003e5.2 What is Artificial Intelligence? 95\u003c\/p\u003e \u003cp\u003e5.2.1 Introduction to Generative Life Cycle 95\u003c\/p\u003e \u003cp\u003e5.3 Generative AI on AWS 98\u003c\/p\u003e \u003cp\u003e5.4 Why Generative AI on AWS? 99\u003c\/p\u003e \u003cp\u003e5.5 How is Generative AI Operational? 101\u003c\/p\u003e \u003cp\u003e5.6 Multiplicative Artificial Intelligence Interfaces 102\u003c\/p\u003e \u003cp\u003e5.7 ChatGPT 102\u003c\/p\u003e \u003cp\u003e5.7.1 How Does ChatGPT Work? 102\u003c\/p\u003e \u003cp\u003e5.7.2 In What Ways is ChatGPT Being Helpful for Users? 103\u003c\/p\u003e \u003cp\u003e5.8 What Advantages Does ChatGPT Offer? 104\u003c\/p\u003e \u003cp\u003e5.8.1 What are ChatGPT’s limitations? To What Extent is it Accurate? 105\u003c\/p\u003e \u003cp\u003e5.9 Dall-e 106\u003c\/p\u003e \u003cp\u003e5.9.1 How DALL-E Works 106\u003c\/p\u003e \u003cp\u003e5.9.2 How Do You Use DALL-E? 107\u003c\/p\u003e \u003cp\u003e5.9.3 How is DALL-E Taught? 108\u003c\/p\u003e \u003cp\u003e5.9.4 The Prospects of ChatGPT and Generative AI 109\u003c\/p\u003e \u003cp\u003e5.9.5 Fields that Utilize DALL-E 110\u003c\/p\u003e \u003cp\u003e5.9.6 Advantages of Using DALL-E to Create Images 111\u003c\/p\u003e \u003cp\u003e5.9.7 DALL-E’s Effect on Image Production 112\u003c\/p\u003e \u003cp\u003e5.9.8 Constraints with DALL-E 112\u003c\/p\u003e \u003cp\u003e5.9.9 Examples of DALL-E’s Use in the Real World 113\u003c\/p\u003e \u003cp\u003e5.9.10 What DALL-E’s Challenges Are 113\u003c\/p\u003e \u003cp\u003e5.10 Bard 114\u003c\/p\u003e \u003cp\u003e5.10.1 What is LaMDA? 114\u003c\/p\u003e \u003cp\u003e5.10.2 How is Google Bard AI Used? 115\u003c\/p\u003e \u003cp\u003e5.10.3 Google Bard AI Features 115\u003c\/p\u003e \u003cp\u003e5.10.4 Examples and Use Cases for Google Bard AI 115\u003c\/p\u003e \u003cp\u003e5.10.5 AI’s Reach with Google Bard 116\u003c\/p\u003e \u003cp\u003e5.10.6 Bard AI by Google vs. ChatGPT 116\u003c\/p\u003e \u003cp\u003e5.10.7 Constraints with Google Bard AI 117\u003c\/p\u003e \u003cp\u003e5.10.8 Important Uses of Generative AI 118\u003c\/p\u003e \u003cp\u003e5.10.9 Creation and Manipulation of Images 118\u003c\/p\u003e \u003cp\u003e5.11 Coding and Software 119\u003c\/p\u003e \u003cp\u003e5.12 Making of Videos 119\u003c\/p\u003e \u003cp\u003e5.13 Creating and Condensing Text 119\u003c\/p\u003e \u003cp\u003e5.14 Interorganizational Cooperation 120\u003c\/p\u003e \u003cp\u003e5.15 Enhancement of Chatbot’s Performance 120\u003c\/p\u003e \u003cp\u003e5.16 Business Exploration 121\u003c\/p\u003e \u003cp\u003e5.17 Conclusion 121\u003c\/p\u003e \u003cp\u003eReferences 122\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Generative AI Unleashed: A Multi-Domain Journey of Successful Implementations of Large Language Models 125\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNikhil Kumar, Anurag Barthwal, Saurabh Mishra and Abhishek Jain\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 126\u003c\/p\u003e \u003cp\u003e6.1.1 Background and Motivation 126\u003c\/p\u003e \u003cp\u003e6.1.1.1 Neural Networks and Deep Learning 127\u003c\/p\u003e \u003cp\u003e6.1.1.2 Transformers 127\u003c\/p\u003e \u003cp\u003e6.1.1.3 Pre-Training and Fine-Tuning 127\u003c\/p\u003e \u003cp\u003e6.1.1.4 Scaling 127\u003c\/p\u003e \u003cp\u003e6.1.2 Scope and Objectives 128\u003c\/p\u003e \u003cp\u003e6.2 Literature Review 128\u003c\/p\u003e \u003cp\u003e6.2.1 Historical Development of Generative Artificial Intelligence 129\u003c\/p\u003e \u003cp\u003e6.2.2 Evolution of LLMs 129\u003c\/p\u003e \u003cp\u003e6.2.3 Applications of Generative AI Across Different Domains 130\u003c\/p\u003e \u003cp\u003e6.2.4 Challenges and Limitations in Implementing LLMs 131\u003c\/p\u003e \u003cp\u003e6.3 Methodology 131\u003c\/p\u003e \u003cp\u003e6.3.1 Research and Design 131\u003c\/p\u003e \u003cp\u003e6.3.2 Methods of Data Collection 131\u003c\/p\u003e \u003cp\u003e6.3.3 Model Selection and Training Techniques 132\u003c\/p\u003e \u003cp\u003e6.3.4 Evaluation Measures 132\u003c\/p\u003e \u003cp\u003e6.3.5 Ethical Considerations 132\u003c\/p\u003e \u003cp\u003e6.4 LLM-Based Case Studies 132\u003c\/p\u003e \u003cp\u003e6.4.1 Natural Language Generation in Healthcare 133\u003c\/p\u003e \u003cp\u003e6.4.1.1 Case Study 1: Patient Diagnosis Support System 133\u003c\/p\u003e \u003cp\u003e6.4.1.2 Case Study 2: Electronic Health Records Summarization 134\u003c\/p\u003e \u003cp\u003e6.4.2 Creative Content in Media and Entertainment 134\u003c\/p\u003e \u003cp\u003e6.4.2.1 Case Study 3: A Scriptwriting Support Tool 134\u003c\/p\u003e \u003cp\u003e6.4.2.2 Case Study 4: Developing Virtual Characters 135\u003c\/p\u003e \u003cp\u003e6.4.3 Language Translation and Multilingual Communication 135\u003c\/p\u003e \u003cp\u003e6.4.3.1 Case Study 5: Multilingual Communication Platform 135\u003c\/p\u003e \u003cp\u003e6.4.3.2 Case Study 6: Real-Time Interpretation Service 136\u003c\/p\u003e \u003cp\u003e6.5 Results and Analysis for LLMs 136\u003c\/p\u003e \u003cp\u003e6.5.1 Performance Evaluation of Implemented Models 136\u003c\/p\u003e \u003cp\u003e6.5.1.1 Quantitative Metrics 137\u003c\/p\u003e \u003cp\u003e6.5.1.2 Qualitative Analysis 139\u003c\/p\u003e \u003cp\u003e6.5.2 Impact Assessment of LLMs Across Different Domains 139\u003c\/p\u003e \u003cp\u003e6.5.2.1 Impact Assessment of LLMs in Healthcare 140\u003c\/p\u003e \u003cp\u003e6.5.2.2 Impact Assessment of LLMs in Infotainment 141\u003c\/p\u003e \u003cp\u003e6.5.2.3 Impact Assessment of LLMs in Language Translation 142\u003c\/p\u003e \u003cp\u003e6.5.3 User Feedback and Acceptance 143\u003c\/p\u003e \u003cp\u003e6.5.3.1 A\/B Testing: Choice as a Coping Strategy 144\u003c\/p\u003e \u003cp\u003e6.5.3.2 Surveys: Capturing Broad Feedback 144\u003c\/p\u003e \u003cp\u003e6.5.3.3 User Interviews: Getting Into the Weeds of UX 144\u003c\/p\u003e \u003cp\u003e6.5.4 Comparison with Existing Systems 145\u003c\/p\u003e \u003cp\u003e6.6 Discussion 145\u003c\/p\u003e \u003cp\u003e6.6.1 Understanding the Successful Implementation of LLMs 145\u003c\/p\u003e \u003cp\u003e6.6.1.1 Multimodal Generative AI: Unleashing the Power of Many Data Types 146\u003c\/p\u003e \u003cp\u003e6.6.2 Challenges and Limitations 147\u003c\/p\u003e \u003cp\u003e6.6.3 Ethical Implications and Responsible AI Practices 148\u003c\/p\u003e \u003cp\u003e6.6.4 Future Directions and Emerging Trends 149\u003c\/p\u003e \u003cp\u003e6.6.4.1 LLMs: A Powerful Tool, But One That Demands Careful Consideration for Society 150\u003c\/p\u003e \u003cp\u003e6.7 Conclusion 151\u003c\/p\u003e \u003cp\u003eReferences 152\u003c\/p\u003e \u003cp\u003eAppendix 155\u003c\/p\u003e \u003cp\u003eGlossary 155\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Misbehaving AI Models and AI Interaction Issues with Humans 157\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNishi Gupta and Shikha Gupta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 158\u003c\/p\u003e \u003cp\u003e7.2 Literature Review 160\u003c\/p\u003e \u003cp\u003e7.3 Misbehaving AI Models 162\u003c\/p\u003e \u003cp\u003e7.3.1 Causes of Misbehaving AI Models 162\u003c\/p\u003e \u003cp\u003e7.3.2 Consequences of Misbehaving AI Models 164\u003c\/p\u003e \u003cp\u003e7.3.3 Mitigation Strategies That Can Be Employed to Address Misbehaving AI Models 167\u003c\/p\u003e \u003cp\u003e7.4 Human Interaction with AI models 168\u003c\/p\u003e \u003cp\u003e7.4.1 Human Interaction Issues with AI Models 168\u003c\/p\u003e \u003cp\u003e7.4.2 Laws Made to Deal with Misbehaving AI Models 169\u003c\/p\u003e \u003cp\u003e7.4.3 The Importance of Ongoing Research and Development in Addressing Misbehaving AI Models 171\u003c\/p\u003e \u003cp\u003e7.5 Conclusion 173\u003c\/p\u003e \u003cp\u003eReferences 174\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Decoding Potential of ChatGPT: A Comprehensive Exploration of AI Generated Contents and Challenges 177\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAnju Kaushik and Anil Kaushik\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 178\u003c\/p\u003e \u003cp\u003e8.2 Chapter Organization 179\u003c\/p\u003e \u003cp\u003e8.3 ChatGPT Popularity Statistics 179\u003c\/p\u003e \u003cp\u003e8.4 Implementation and Work Flow of ChatGPT 180\u003c\/p\u003e \u003cp\u003e8.5 ChatGPT Key Characteristics in Present Scenario 182\u003c\/p\u003e \u003cp\u003e8.6 Potential Challenges 186\u003c\/p\u003e \u003cp\u003e8.7 Security Threats in ChatGPT 187\u003c\/p\u003e \u003cp\u003e8.8 ChatGPT’s Privacy Risks 189\u003c\/p\u003e \u003cp\u003e8.9 Ethical Concern 192\u003c\/p\u003e \u003cp\u003e8.10 Computer Ethics Challenges Raised by ChatGPT 194\u003c\/p\u003e \u003cp\u003e8.11 Limitation of ChatGPT 195\u003c\/p\u003e \u003cp\u003e8.12 Balance Between Human Knowledge and AI-Supported Innovation 196\u003c\/p\u003e \u003cp\u003e8.13 Future Challenges 197\u003c\/p\u003e \u003cp\u003e8.14 Conclusion 197\u003c\/p\u003e \u003cp\u003eReferences 198\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Economizing Large Language Model Training and Alignment with Human Values through Cost Effective Architectures and Transfer Learning Techniques 201\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMohammed Wasim Bhatt, Rubal Jeet, Mukesh Soni, Haewon Byeon and Vishal Sagar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 202\u003c\/p\u003e \u003cp\u003e9.2 Literature Survey 203\u003c\/p\u003e \u003cp\u003e9.3 Proposed Method 205\u003c\/p\u003e \u003cp\u003e9.4 Results 216\u003c\/p\u003e \u003cp\u003e9.5 Discussion 219\u003c\/p\u003e \u003cp\u003e9.6 Conclusion 219\u003c\/p\u003e \u003cp\u003eReferences 220\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 3: In-Context Learning\/Prompt Engineering 223\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 From Prompts to Performance: Innovations in Context Learning 225\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAmandeep Sharma, Prince Kumar and Shashank Dhamija\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 The Art of Prompt Engineering: A Deep Dive 226\u003c\/p\u003e \u003cp\u003e10.1.1 Core Definitions and Key Concepts of Prompt Engineering 226\u003c\/p\u003e \u003cp\u003e10.1.1.1 Significance of Prompt Engineering 226\u003c\/p\u003e \u003cp\u003e10.1.1.2 Fundamental Components of a Prompt 226\u003c\/p\u003e \u003cp\u003e10.1.1.3 Prompt Engineering’s Technical Aspects 228\u003c\/p\u003e \u003cp\u003e10.2 Strategies for Crafting Effective Prompts 229\u003c\/p\u003e \u003cp\u003e10.3 Techniques for Controlling the Model Behavior and Output 245\u003c\/p\u003e \u003cp\u003e10.4 Best Practices for Prompt Engineering 246\u003c\/p\u003e \u003cp\u003e10.4.1 Prompt Engineering Principles 247\u003c\/p\u003e \u003cp\u003e10.4.2 Structured Procedure Behind Prompt Engineering 247\u003c\/p\u003e \u003cp\u003e10.4.3 Prompt Engineering Use Cases and Applications 248\u003c\/p\u003e \u003cp\u003eReferences 250\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 4: LangChain Framework 253\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Introduction to LangChain Framework 255\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDeepti Goyal and Amita Gautam\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction of LangChain Framework 256\u003c\/p\u003e \u003cp\u003e11.2 Large Language Model (LLM) 258\u003c\/p\u003e \u003cp\u003e11.3 What Do You Mean by Chains in LangChain Framework 260\u003c\/p\u003e \u003cp\u003e11.3.1 Various Types of Chains 260\u003c\/p\u003e \u003cp\u003e11.3.1.1 LLMChain 261\u003c\/p\u003e \u003cp\u003e11.3.1.2 Router Chain 261\u003c\/p\u003e \u003cp\u003e11.3.1.3 Sequential Chain 262\u003c\/p\u003e \u003cp\u003e11.4 Why LangChain Framework is Important 263\u003c\/p\u003e \u003cp\u003e11.5 Main Components of LangChain Framework 264\u003c\/p\u003e \u003cp\u003e11.5.1 Large Language Model (LLM) 264\u003c\/p\u003e \u003cp\u003e11.5.2 Prompt Template 265\u003c\/p\u003e \u003cp\u003e11.5.2.1 Indexes 265\u003c\/p\u003e \u003cp\u003e11.5.2.2 Retriever 265\u003c\/p\u003e \u003cp\u003e11.5.2.3 Parsers for Output 265\u003c\/p\u003e \u003cp\u003e11.5.2.4 Vector Store 266\u003c\/p\u003e \u003cp\u003e11.5.2.5 Agents 266\u003c\/p\u003e \u003cp\u003e11.5.2.6 Memory 266\u003c\/p\u003e \u003cp\u003e11.5.2.7 Chain 267\u003c\/p\u003e \u003cp\u003e11.6 Feature of LangChain Framework 267\u003c\/p\u003e \u003cp\u003e11.6.1 Scalability 267\u003c\/p\u003e \u003cp\u003e11.6.2 Improved Usability 267\u003c\/p\u003e \u003cp\u003e11.6.3 Adaptability 267\u003c\/p\u003e \u003cp\u003e11.6.4 Extension 267\u003c\/p\u003e \u003cp\u003e11.6.5 External Integrations 268\u003c\/p\u003e \u003cp\u003e11.6.6 Thriving Community 268\u003c\/p\u003e \u003cp\u003e11.6.7 Flexibility Across Zones 268\u003c\/p\u003e \u003cp\u003e11.6.8 Integrations 268\u003c\/p\u003e \u003cp\u003e11.6.9 Standardized Interfaces 268\u003c\/p\u003e \u003cp\u003e11.6.10 Prompt Management and Optimization 268\u003c\/p\u003e \u003cp\u003e11.6.11 Visualization and Experimentation 268\u003c\/p\u003e \u003cp\u003e11.7 How to Install 269\u003c\/p\u003e \u003cp\u003e11.7.1 Steps to Develop an Application in LangChain Framework 270\u003c\/p\u003e \u003cp\u003e11.7.1.1 Describe the Use Case 270\u003c\/p\u003e \u003cp\u003e11.7.1.2 Develop Functionality 270\u003c\/p\u003e \u003cp\u003e11.7.1.3 Tailor the Functionality 270\u003c\/p\u003e \u003cp\u003e11.7.1.4 Optimizing LLMs 270\u003c\/p\u003e \u003cp\u003e11.7.1.5 Data Purification 270\u003c\/p\u003e \u003cp\u003e11.7.1.6 Experimenting 271\u003c\/p\u003e \u003cp\u003e11.7.2 Build a New Application with LangChain Framework 271\u003c\/p\u003e \u003cp\u003e11.8 Real World Applications with LangChain Framework 272\u003c\/p\u003e \u003cp\u003e11.8.1 LangSmith 272\u003c\/p\u003e \u003cp\u003e11.8.2 Chatbots 272\u003c\/p\u003e \u003cp\u003e11.8.3 Automated Blog Outlines 272\u003c\/p\u003e \u003cp\u003e11.8.4 Integration with MongoDB Atlas 272\u003c\/p\u003e \u003cp\u003e11.8.5 Medical Care 272\u003c\/p\u003e \u003cp\u003e11.8.6 Help with Coding 273\u003c\/p\u003e \u003cp\u003e11.8.7 Creating Condensed Content 273\u003c\/p\u003e \u003cp\u003e11.9 Integration of LangChain Framework 273\u003c\/p\u003e \u003cp\u003e11.10 Creating a Prompt in LangChain Framework 274\u003c\/p\u003e \u003cp\u003e11.10.1 Types of LangChain Prompts 275\u003c\/p\u003e \u003cp\u003e11.10.2 Prompt Template 275\u003c\/p\u003e \u003cp\u003e11.10.3 Few_Shot_Prompt_Template 276\u003c\/p\u003e \u003cp\u003e11.10.4 Chat_Prompt_Template 276\u003c\/p\u003e \u003cp\u003e11.11 Future of LangChain Framework with AI Enabled Tools 278\u003c\/p\u003e \u003cp\u003e11.11.1 ChatGPT and Chatbots 278\u003c\/p\u003e \u003cp\u003e11.11.2 AI-Powered Text Categorization Tools 278\u003c\/p\u003e \u003cp\u003e11.11.3 False References 279\u003c\/p\u003e \u003cp\u003e11.12 Limitation of LangChain Framework 279\u003c\/p\u003e \u003cp\u003e11.13 Alternative Technologies Apart from LangChain Framework Used in 2024 280\u003c\/p\u003e \u003cp\u003e11.13.1 Auto-GPT: Bringing AI Agent Development to New Heights 280\u003c\/p\u003e \u003cp\u003e11.13.2 Prompt_Chainer 281\u003c\/p\u003e \u003cp\u003e11.13.3 Auto_Chain 282\u003c\/p\u003e \u003cp\u003e11.13.4 AgentGPT: Unleashing the Power of Autonomous AI Agents 282\u003c\/p\u003e \u003cp\u003e11.13.5 BabyAGI: A Glimpse Into the Future of Task-Driven AI 283\u003c\/p\u003e \u003cp\u003e11.13.6 SimpleaiChat 283\u003c\/p\u003e \u003cp\u003e11.13.7 GradientJ: Building LLM-Powered Applications with Ease 284\u003c\/p\u003e \u003cp\u003e11.14 Conclusion 284\u003c\/p\u003e \u003cp\u003eReferences 285\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 LangChain: Simplifying Development with Language Models 287\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSangeetha Annam, Merry Saxena, Ujjwal Kaushik and Shikha Mittal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 288\u003c\/p\u003e \u003cp\u003e12.2 Phases and Characteristics of LLM Application 289\u003c\/p\u003e \u003cp\u003e12.3 Components and Key Elements of LLM 290\u003c\/p\u003e \u003cp\u003e12.4 Types and Architecture of LLM 293\u003c\/p\u003e \u003cp\u003e12.5 Benefits and Approaches of LLM 296\u003c\/p\u003e \u003cp\u003e12.6 Building an LLM Application 299\u003c\/p\u003e \u003cp\u003e12.7 Use Cases 300\u003c\/p\u003e \u003cp\u003eReferences 302\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Addressing Ethical Challenges in LLMs: Bias and Misinformation 305\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePummy Dhiman and Amandeep Kaur\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 305\u003c\/p\u003e \u003cp\u003e13.2 LLM Evolution Tree 308\u003c\/p\u003e \u003cp\u003e13.2.1 Bert 309\u003c\/p\u003e \u003cp\u003e13.2.2 Gpt 311\u003c\/p\u003e \u003cp\u003e13.3 Types of LLMs 313\u003c\/p\u003e \u003cp\u003e13.4 Limitations of LLMs 314\u003c\/p\u003e \u003cp\u003e13.5 Factors Contributing to Bias and Misinformation Generation 316\u003c\/p\u003e \u003cp\u003e13.6 Methods to Address Bias and Misinformation 317\u003c\/p\u003e \u003cp\u003e13.7 Conclusion 319\u003c\/p\u003e \u003cp\u003eReferences 320\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 5: LLM-Powered Applications 323\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 LegalEase: Application Development with LangChain Framework 325\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNidhi Malik, Lakshita Chhikara, Abhilakshay and Ambika Thakur\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 325\u003c\/p\u003e \u003cp\u003e14.1.1 Large Language Model 326\u003c\/p\u003e \u003cp\u003e14.1.2 General Architecture 327\u003c\/p\u003e \u003cp\u003e14.1.3 Examples of LLMs 329\u003c\/p\u003e \u003cp\u003e14.1.4 Benefits 329\u003c\/p\u003e \u003cp\u003e14.1.5 Industry Applications 330\u003c\/p\u003e \u003cp\u003e14.2 LangChain 331\u003c\/p\u003e \u003cp\u003e14.2.1 Key Features of LangChain 331\u003c\/p\u003e \u003cp\u003e14.2.2 Key Components 333\u003c\/p\u003e \u003cp\u003e14.2.3 Who Should Explore 335\u003c\/p\u003e \u003cp\u003e14.3 Example of Application Development 335\u003c\/p\u003e \u003cp\u003e14.3.1 Key Features 336\u003c\/p\u003e \u003cp\u003e14.3.2 Purpose and Benefits 336\u003c\/p\u003e \u003cp\u003e14.4 Development Steps 337\u003c\/p\u003e \u003cp\u003e14.4.1 Libraries and Imports 337\u003c\/p\u003e \u003cp\u003e14.4.2 Environment Setup 340\u003c\/p\u003e \u003cp\u003e14.4.3 Data Collection 341\u003c\/p\u003e \u003cp\u003e14.4.4 User Interface Setup 342\u003c\/p\u003e \u003cp\u003e14.4.5 Document Summarization 343\u003c\/p\u003e \u003cp\u003e14.4.6 Querying the Document 355\u003c\/p\u003e \u003cp\u003e14.5 Conclusion 362\u003c\/p\u003e \u003cp\u003eReferences 363\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Unveiling the Potential of Massive Language Models in Software Engineering: Exploring Opportunities, Addressing Risks, and Comprehending Implications 365\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMitali Chugh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 366\u003c\/p\u003e \u003cp\u003e15.2 Harnessing the Power: Abilities of Large Language Models 367\u003c\/p\u003e \u003cp\u003e15.3 Navigating Challenges: Risks and Ethical Considerations 369\u003c\/p\u003e \u003cp\u003e15.4 Ethical Application: Strategies and Frameworks 371\u003c\/p\u003e \u003cp\u003e15.5 Establishing Ethical Frameworks for Accountability 372\u003c\/p\u003e \u003cp\u003e15.6 Collaborative Standards: Industry and Research Collaboration 373\u003c\/p\u003e \u003cp\u003e15.7 Transformative Effects: Broader Implications in Software Engineering 375\u003c\/p\u003e \u003cp\u003e15.8 Shaping the Future: Prospective Directions of Large Language Models 377\u003c\/p\u003e \u003cp\u003e15.9 Conclusion 378\u003c\/p\u003e \u003cp\u003eReferences 379\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Multidimensional Impacts of Generative AI and an In-Depth Analysis of LLMs with Their Expanding Horizons in Technology and Society 383\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRubal Jeet, Mohammed Wasim Bhatt, Maher Ali Rusho, Aadam Quraishi and Mahesh Manchanda\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 384\u003c\/p\u003e \u003cp\u003e16.2 Literature Review 386\u003c\/p\u003e \u003cp\u003e16.3 Proposed Methodology 389\u003c\/p\u003e \u003cp\u003e16.4 Results 402\u003c\/p\u003e \u003cp\u003e16.5 Conclusion 408\u003c\/p\u003e \u003cp\u003eReferences 409\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 6: Responsible AI 413\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Responsible AI: Ethical Considerations in Generative AI 415\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKamal Kumar and Poonam\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 416\u003c\/p\u003e \u003cp\u003e17.1.1 Defining Generative AI 416\u003c\/p\u003e \u003cp\u003e17.1.2 Distinguishing Machine Learning Approaches 417\u003c\/p\u003e \u003cp\u003e17.1.3 Brief History and Recent Breakthroughs 417\u003c\/p\u003e \u003cp\u003e17.1.4 Overview of Key Generative Architectures and Techniques 420\u003c\/p\u003e \u003cp\u003e17.1.4.1 Autoregressive Models 420\u003c\/p\u003e \u003cp\u003e17.1.4.2 Generative Adversarial Networks (GANs) 420\u003c\/p\u003e \u003cp\u003e17.1.4.3 VariationalAutoencoders (VAEs) 420\u003c\/p\u003e \u003cp\u003e17.1.4.4 Diffusion Models 421\u003c\/p\u003e \u003cp\u003e17.1.4.5 Self-Supervised, Meta and Multi-Task Learning 422\u003c\/p\u003e \u003cp\u003e17.1.5 Promising Applications and Benefits 422\u003c\/p\u003e \u003cp\u003e17.2 Key Ethical Considerations, Risks, and Challenges 423\u003c\/p\u003e \u003cp\u003e17.2.1 Societal Biases and Unfair Representational Harms 423\u003c\/p\u003e \u003cp\u003e17.2.2 Truth Manipulation and Attribution Difficulties 424\u003c\/p\u003e \u003cp\u003e17.2.3 Violations of Consent, Privacy, and Agency 424\u003c\/p\u003e \u003cp\u003e17.2.4 Misuse Potentials Across Fraud, Deceit, and Sabotage 424\u003c\/p\u003e \u003cp\u003e17.2.5 Broader Societal Impacts on Economics, Culture and Psychology 425\u003c\/p\u003e \u003cp\u003e17.3 Guiding Principles and Frameworks for Responsible Generative AI 425\u003c\/p\u003e \u003cp\u003e17.3.1 Transparency 426\u003c\/p\u003e \u003cp\u003e17.3.2 Justice, Fairness, and Inclusion 426\u003c\/p\u003e \u003cp\u003e17.3.3 Non-Maleficence 426\u003c\/p\u003e \u003cp\u003e17.3.4 Responsibility and Accountability 426\u003c\/p\u003e \u003cp\u003e17.3.5 Privacy and Data Protection 426\u003c\/p\u003e \u003cp\u003e17.4 Governance Strategies for Trustworthy Generative AI Innovation 427\u003c\/p\u003e \u003cp\u003e17.4.1 AI Ethics Guidelines and Organizational Policies 427\u003c\/p\u003e \u003cp\u003e17.4.2 Laws, Regulations, and Dynamic Governance Complexities 427\u003c\/p\u003e \u003cp\u003e17.4.3 Technical Approaches to Fairness, Transparency and Control 427\u003c\/p\u003e \u003cp\u003e17.4.4 Stakeholder Participation and Public Discourse Ethics 428\u003c\/p\u003e \u003cp\u003e17.5 Recommendations for Key Generative AI Stakeholders 428\u003c\/p\u003e \u003cp\u003e17.5.1 Guidelines for Technology Researchers and Developers 428\u003c\/p\u003e \u003cp\u003e17.5.2 Strategies for Organizations, Platforms, and Corporations 429\u003c\/p\u003e \u003cp\u003e17.5.3 Ethical Governance Strategies for Organizations 429\u003c\/p\u003e \u003cp\u003e17.5.4 Policy Options for Governments and Lawmakers 429\u003c\/p\u003e \u003cp\u003e17.5.5 Priorities for Broader Industry Governance Entities 430\u003c\/p\u003e \u003cp\u003e17.5.6 Considerations for Civil Society Groups, Activists, and General Public 430\u003c\/p\u003e \u003cp\u003e17.5.7 The Impact of Generative AI Like ChatGPT on Education 430\u003c\/p\u003e \u003cp\u003eSignificant Risks and Difficulties to Surmount 431\u003c\/p\u003e \u003cp\u003eResearch Priorities for the Future 431\u003c\/p\u003e \u003cp\u003e17.6 Conclusions 432\u003c\/p\u003e \u003cp\u003eReferences 433\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 From Prototyping to Deployment: Human-Centered Design Practices in Responsible AI Innovation 435\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJyoti Snehi, Manish Snehi, Isha Kansal and Vikas Khullar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 436\u003c\/p\u003e \u003cp\u003e18.2 Literature Review 441\u003c\/p\u003e \u003cp\u003eOverview of Human-Centered Design Principles 443\u003c\/p\u003e \u003cp\u003eResponsible AI 447\u003c\/p\u003e \u003cp\u003eGaps in Existing Research 451\u003c\/p\u003e \u003cp\u003eMethodology 452\u003c\/p\u003e \u003cp\u003eResearch Design 452\u003c\/p\u003e \u003cp\u003eRationale for Qualitative Approach 452\u003c\/p\u003e \u003cp\u003eHuman-Centered Design in AI Prototyping 456\u003c\/p\u003e \u003cp\u003eDistinctions and Issues 456\u003c\/p\u003e \u003cp\u003eUser Research and Personas 456\u003c\/p\u003e \u003cp\u003eEarly-Phase Prototyping 457\u003c\/p\u003e \u003cp\u003eIterative Design and Feedback Loops 457\u003c\/p\u003e \u003cp\u003eEthical Considerations in AI Prototyping 458\u003c\/p\u003e \u003cp\u003eIdentifying Ethical Challenges 458\u003c\/p\u003e \u003cp\u003eIncorporating Ethical Guidelines Into Prototyping 458\u003c\/p\u003e \u003cp\u003eCase Studies of Ethical AI Prototyping 459\u003c\/p\u003e \u003cp\u003eFrom Prototyping to Development 459\u003c\/p\u003e \u003cp\u003eTransitioning From Prototype to Full Development 460\u003c\/p\u003e \u003cp\u003eEnsuring Consistency in HCD Practices 460\u003c\/p\u003e \u003cp\u003eCollaboration Across Multidisciplinary Teams 461\u003c\/p\u003e \u003cp\u003eTools and Techniques for Managing Development Phases 461\u003c\/p\u003e \u003cp\u003eHuman-Centered Design in AI Deployment 462\u003c\/p\u003e \u003cp\u003eChallenges and Solutions 463\u003c\/p\u003e \u003cp\u003eCommon Challenges in Implementing HCD in AI 463\u003c\/p\u003e \u003cp\u003eSolutions and Best Practices 465\u003c\/p\u003e \u003cp\u003eLessons Learned From Case Studies 467\u003c\/p\u003e \u003cp\u003eFramework for Human-Centered and Responsible AI 469\u003c\/p\u003e \u003cp\u003e18.3 Conclusion 471\u003c\/p\u003e \u003cp\u003eReferences 472\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Toward Accurate Abbreviation Disambiguation in Medical Texts: A Comparative Study of AI Models 475\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eA. Pandey and M. Saini\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 476\u003c\/p\u003e \u003cp\u003e19.2 Related Work 477\u003c\/p\u003e \u003cp\u003e19.3 Datasets 479\u003c\/p\u003e \u003cp\u003e19.4 Methodology 480\u003c\/p\u003e \u003cp\u003e19.4.1 Data Collection 481\u003c\/p\u003e \u003cp\u003e19.4.2 Pre-Processing 481\u003c\/p\u003e \u003cp\u003e19.4.3 Vector Feature Extraction 482\u003c\/p\u003e \u003cp\u003e19.4.4 Classification Model 484\u003c\/p\u003e \u003cp\u003e19.5 Results and Discussion 488\u003c\/p\u003e \u003cp\u003e19.6 Conclusion 491\u003c\/p\u003e \u003cp\u003eReferences 491\u003c\/p\u003e \u003cp\u003eIndex 495\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20science%20%5BUY%5D%22\"\u003eUY\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52433295704344,"sku":"9781394287468","price":163.59,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394287468.jpg?v=1784853174","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/textual-intelligence-large-language-models-and-their-real-world-applications-hardback-9781394287468","provider":"Freshly Printed Books","version":"1.0","type":"link"}