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Large Language Models and Architecture Evolution 1

Edited by Krishan Arora, Balraj Singh, Suman Lata Tripathi, and Mufti Mahmud
Copyright: 2026   |   Expected Pub Date: 2026
ISBN: 9781394467655  |  Hardcover  |  
384 pages
Price: $225 USD
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One Line Description
Master the theoretical mechanics and real-world deployment of modern AI with this definitive guide, giving you the exact technical depth needed to build, evaluate, and innovate with state-of-the-art Large Language Model systems.

Description
In the last decade, the landscape of natural language processing has shifted dramatically, evolving from foundational architectures into complex, high-impact systems driving technological transformation across industries. This volume provides a comprehensive exploration of Large Language Models (LLMs), bridging foundational mechanics with state-of-the-art advances for undergraduate and graduate students, researchers, and technical professionals. Organized into three cohesive sections, the book traces the trajectory of LLMs from fundamental underlying mechanisms to advanced paradigms, including Retrieval-Augmented Generation (RAG), long-context modeling, and industry-standard integrations like DeepSeek and AutoGPT. Beyond core theoretical principles and real-world case studies, it delivers a critical analysis of current system limitations, safety bottlenecks, and practical deployment constraints. Serving as a definitive reference for academia and industry alike, this text equips readers with the holistic understanding required to evaluate, innovate, and deploy modern LLM technologies effectively. 

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Author / Editor Details
Krishan Arora, PhD is the Head of the Department of Power Systems in the School of Electronics and Electrical Engineering at Lovely Professional University, India, with more than seventeen years of experience in academics and research. He has published more than 85 research papers in refereed journals and conferences, five edited books, six Indian patents, and a copyright. He has edited 5 books in different areas of Electronics and Electrical engineering. His expertise lies in power electronics, non-conventional energy sources, electric drives, induction and synchronous machines, and digital electronics.

Balraj Singh, PhD is a Professor in the School of Computer Science and Engineering at Lovely Professional University, India. He has published more than 40 research papers in various journals, conferences, and book chapters, as well as several patents. His research areas include distributed systems, software engineering, and networks.

Suman Lata Tripathi, PhD is a Professor at Lovely Professional University, India, with more than 20 years of experience in academics. She has published more than 74 research papers in refereed journals and conferences, 17 books, 13 Indian patents, and two copyrights. Her area of expertise includes microelectronics device modeling and characterization, low-power VLSI circuit design, VLSI design of testing, and advanced FET design for IoT.

Mufti Mahmud is a Professor in the Department of Information and Computer Science at the King Fahd University of Petroleum and Minerals, Saudi Arabia. He has an extensive international research profile, with more than 350 peer-reviewed publications and substantial research funding. His research expertise includes cognitive computing, artificial intelligence in healthcare, neuroengineering, brain informatics, and responsible AI.

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Table of Contents
Preface
1. Evolution in Large Language Models
Priyanka Bhore, Kasim Lohar and Himanshu Lohokane
1.1 Introduction
1.1.1 The Promise of Universal Language Understanding
1.1.2 Scope and Objectives
1.1.3 Democratization Framework
1.2 Background and Related Work
1.2.1 Theoretical Foundations
1.2.2 Literature Survey
1.2.3 Methodological Framework
1.3 Era 1: Expert Systems – Language AI for Specialists (1950s–1990s)
1.3.1 Rule-Based Foundations
1.3.2 Statistical Emergence
1.3.3 Era 1 Assessment
1.4 Era 2: Statistical Renaissance – Language AI for Researchers (1990s–2017)
1.4.1 Neural Network Revolution
1.4.2 Contextualized Representations
1.4.3 Pre-Transformer Architectures
1.4.4 Era 2 Assessment
1.5 Era 3: Transformer Revolution – Language AI for Everyone
(2017–2024)
1.5.1 The Attention Breakthrough (2017–2019)
1.5.2 Scaling Era (2019–2022)
1.5.3 Modern LLM Landscape (2022–2024)
1.5.4 Democratization Metrics
1.6 Synthesis: Patterns of Democratization
1.6.1 Technical Enablers
1.6.2 Economic Transformation
1.6.3 Capability Emergence
1.6.4 Adoption Patterns
1.7 Future Directions and Implications
1.7.1 Next Democratization Wave
1.7.2 Technical Frontiers
1.7.3 Research Challenges
1.8 Conclusion
1.8.1 Key Contributions
1.8.2 Core Insights
1.8.3 Implications for Practice
Bibliography
2. Federated Learning: Concepts, Architecture, and Applications across Different Domains
Alka Singh, Gagandeep Kaur, Anshu Vashisth and Balraj Singh
2.1 Introduction
2.2 Literature Review
2.3 Architecture of Federated Learning
2.4 Applications of Federated Learning
2.5 Challenges of Federated Learning
2.6 Future Scope
Conclusion
References
3. Leveraging Large Language Models for Personalized Education: Opportunities and Challenges
Geetika, Aarti and Richa Sharma
3.1 Introduction
3.2 Related Work
3.3 Research Methodology
3.3.1 Study Design
3.3.2 Data Collection
3.3.3 LLM Integration Framework
3.3.4 Performance Index (PI) and Engagement Score (ES)
3.4 Results
3.4.1 Quantitative Findings
3.4.2 Qualitative Insights
3.4.3 Case Study Outcomes
3.5 Addressing Research Questions
3.5.1 RQ1: How Might LLMs Produce Personalized Learning Experiences within Higher Education
3.5.2 RQ2: What are the Ethical Challenges Associated with LLMs’ Inclusion in Educational Systems
3.5.3 RQ3: What are the Practical Challenges of LLM Deployment in Educational Settings
3.5.4 RQ4: How Do LLM’s Affect Student Engagement and Learning Outcomes
3.5.5 RQ5: What are the Best Approaches to Link LLM’s with Another Educational Technology to Enhance Scale
3.5.6 RQ6: What Can Be Done to Ensure Educators’ Readiness for Integrating LLM’s into Teaching and Learning Practices
3.6 Discussion on Opportunities, Challenges, and Recommendations
3.6.1 Opportunities
3.6.2 Challenges
3.6.3 Recommendations
3.7 Conclusion
References
4. Advanced Prompt Engineering Strategies for Large Language Models: Architectures, Optimization Techniques, and Application-Specific Adaptations
Manik Rakhra and Tiyas Sarkar
4.1 Background and Objectives
4.2 Introduction
4.3 Literature Review
4.4 Problem Statement
4.5 Research Objectives
4.6 Proposed Framework
4.7 Evaluation Setup and Discussions
4.8 Discussion
4.9 Conclusions
References
5. Cache-Augmented Generation (CAG): Leveraging Memory and Context Retention for Efficient Language Models
Manik Rakhra and Tiyas Sarkar
5.1 Background and Objectives
5.2 Introduction
5.3 Literature Review
5.4 Problem Statement
5.5 Research Objectives
5.6 Proposed Framework
5.7 Evaluation Setup
5.8 Discussions
5.9 Conclusions
5.10 Future Work
References
6. Large Language Models in Healthcare: Financial and Economic Perspectives
Ujan Pradhan, Sukanya Singh and Vyomika Anand
6.1 Introduction
6.2 Fundamentals of LLMs in Healthcare
6.3 Financial and Economic Framework for Evaluation
6.3.1 Cost-Benefit and ROI Analysis
6.3.2 Broader Economic and Policy Evaluation
6.4 LLM Application Areas and Financial Implications
6.4.1 Clinical Decision Support and Diagnostics
6.4.2 Administrative and Operational Efficiency
6.4.3 Patient Engagement and Preventive Care
6.5 Market Dynamics, Business Models, and Macroeconomic Impact
6.5.1 Market Dynamics
6.5.2 Business Models
6.5.3 Macroeconomic Impact
6.6 Barriers, Risks, and Mitigation Strategies
6.6.1 Technological Barriers and Risks
6.6.2 Ethical, Legal, and Regulatory Barriers
6.6.3 Financial and Economic Barriers
6.6.4 Organizational and Human-Centric Barriers
6.7 Case Studies
6.7.1 Case Study 1: LLMs in Hospital Administration and Clinical Documentation
6.7.2 Case Study 2: LLMs in Pharmaceutical Research and Drug Discovery
6.7.3 Case Study 3: LLMs in Health Insurance and Claims Management
6.7.4 Synthesis of Case Studies
6.8 Future Directions and Conclusions
6.8.1 Rethinking Healthcare Economics in the Era of LLMs
6.8.2 Emerging Business Models and Market Dynamics
6.8.3 Policy, Regulation, and Economic Governance
6.8.4 Future Research and Innovation Pathways
6.8.5 Ethical and Socioeconomic Considerations
6.8.6 Vision for the Next Decade
References
7. LLMS in Education
Kusum Tilkar, Alka Karketta, Deepak Rathod, Vijay Chauhan and Gyanesh Savita
7.1 Introduction
7.1.1 Contributions and Scope
7.2 Literature Review
7.2.1 Personalized Language Learning Platforms
7.2.2 Adaptive Courseware in Higher Education
7.2.3 K-12 Supplemental Education Tools
7.2.4 Professional Development Platforms
7.2.5 Special Education Support Tools
7.3 Technical Foundations of LLMs in Education
7.3.1 Architecture and Capabilities
7.3.2 Educational Agent Framework
7.4 Core Features and Applications
7.4.1 Personalized Learning Implementation
7.4.2 Educational Content Creation
7.4.3 Adaptive Learning Platform Integration
7.5 Enhancing Teacher-Student Interactions
7.5.1 Personalized Feedback Systems
7.5.2 Administrative Workload Reduction
7.6 Lifelong Learning and Professional Development
7.6.1 Continuous Skill Development
7.6.2 Professional Development Applications
7.7 Challenges and Barriers
7.7.1 Implementation Challenges
7.7.2 Mitigation Strategies
7.8 Case Studies and Success Stories
7.8.1 Language Learning Platform Success
7.8.2 University Adaptive Courseware
7.8.3 K-12 Supplemental Education Tool
7.8.4 Professional Development Platform
7.8.5 Special Education Support
7.9 Future Directions and Emerging Trends
7.9.1 Technological Advancements
7.9.2 Emerging Applications
7.10 Ethical Considerations and Responsible Deployment
7.10.1 Core Ethical Challenges
7.10.2 Framework for Responsible Implementation
7.11 Datasets, Benchmarks, and Evaluation Frameworks
7.11.1 Educational Dataset Categories
7.11.2 Evaluation Benchmarks
7.12 Conclusion
References
8. Large Language Model Application Programming Interfaces
(LLM APIs)
Hansa Rajput
8.1 Introduction
8.2 Fundamentals of APIs
8.2.1 Definition and Types of APIs
8.2.2 RESTful APIs vs. GraphQL APIs in AI Contexts
8.2.3 API Authentication and Access Protocols
8.3 Understanding LLM APIs
8.3.1 Large Language Model (LLM) APIs
8.3.2 Common Features Offered by LLM APIs
8.3.3 Input/Output Format and Token Constraints
8.4 Popular LLM APIs in Practice
8.4.1 OpenAI (ChatGPT, GPT-4, GPT-4o)
8.4.2 Google PaLM API/Gemini
8.4.3 Anthropic Claude API
8.4.4 Cohere and Hugging Face Inference API
8.5 Architecture and Integration
8.5.1 How LLM APIs are Architecturally Integrated
8.5.2 Cloud-Based Deployment and Edge-Device Interactions
8.5.3 Security, Latency, and Cost Considerations
8.6 Use Cases across Domains
8.6.1 Education: Intelligent Tutoring Systems
8.6.2 Healthcare: Medical Q&A and Documentation
8.6.3 Business: Customer Support Automation and Report Generation
8.6.4 Programming: Code Completion and Debugging Assistants
8.7 Building with LLM APIs: A Developer’s Perspective
8.8 Challenges and Limitations
8.8.1 Data Privacy and Ethical Concerns
8.8.2 Hallucinations and Factual Reliability
8.8.3 Cost Control in High-Volume Applications
8.8.4 Token Limits and Truncation Issues
8.9 Future Directions
8.9.1 API Fine-Tuning and Personalization Capabilities
8.9.2 Multi-Modal API Evolution (Text + Vision + Speech)
8.9.3 Open-Source LLM APIs and Community-Driven Development
8.10 Conclusion
Bibliography
9. Large Language Models in Learning and Development
Shrish Bajpai and Divya Sharma
9.1 Introduction
9.2 Review of Large Language Models
9.2.1 Transformers
9.2.2 Large Foundational Models
9.2.3 Multimodal Language Models
9.3 Large Language Models in Learning and Development
9.3.1 Personalized Learning
9.3.2 Automated Grading and Feedback
9.3.3 Content Creation and Enhancement
9.3.4 Student Support and Engagement
9.3.5 Administrative Efficiency
9.3.6 Accessibility and Inclusivity
9.3.7 Research and Academic Assistance
9.4 Limitations and Challenges
9.5 Conclusion
Acknowledgements
References
10. Large Language Models for Industry 5.0
Pankaj Kumar and S. K. Singh
10.1 Introduction
10.1.1 Human–AI Collaboration
10.1.2 Predictive Maintenance and Process Optimization
10.1.3 Personalization and Mass Customization
10.1.4 Intelligent Automation and Hyper-Automation
10.1.5 Skill Enhancement and Training
10.1.6 Sustainability and Circular Economy
10.2 Industrial Revolution
10.3 Industrial Revolution and Large Language Models (LLMs)
10.4 Literature Survey
10.5 The Impact of LLMs Deployment in Industry 5.0
10.6 LLMs and GenAI Use Cases in Industry 5.0
10.7 Benefits of LLMs Deployment in Industry 5.0
10.8 Drawbacks of LLMs Deployment in Industry 5.0
10.9 Conclusion
References
11. Large Language Models in Scientific Research
Richa Sharma, Aarti and Geetika
11.1 Introduction
11.2 Overview of Large Language Models
11.3 Applications of LLMs in Scientific Research
11.3.1 Literature Review and Hypothesis Generation
11.3.2 Experiment Design and Execution
11.3.3 Data Analysis and Interpretation
11.3.4 Scientific Writing and Communication
11.3.5 Domain-Specific Applications
11.3.5.1 Medicine and Healthcare
11.3.5.2 Chemistry
11.3.5.3 Other Fields
11.4 Advantages and Challenges
11.4.1 Advantages
11.4.2 Challenges
11.5 Case Studies
11.5.1 ChemCrow in Chemistry
11.5.2 Med-PaLM in Medicine
11.5.3 The AI Scientist in Machine Learning
11.6 Conclusion
References
12. Large Language Models in Healthcare
Aarti, Geetika and Richa Sharma
12.1 Introduction
12.2 Foundations of Large Language Models
12.3 Applications of LLMs in Healthcare
12.3.1 Clinical Decision Support (CDS)
12.3.2 EHR Summarization and Documentation
12.3.3 Drug Discovery and Genomics
12.3.4 Patient-Facing Applications and Triage
12.3.5 Multimodal Models for Imaging Text Tasks
12.4 Comparative Results from Published Studies
12.5 Challenges and Limitations
12.5.1 Bias and Fairness
12.5.2 Privacy and Security
12.5.3 Reliability and Hallucinations
12.5.4 Evaluation and Generalizability
12.5.5 Regulation and Liability
12.6 Future Directions
12.7 Conclusion
Bibliography
13. Reconstructing the Education Architecture: Incubating Large Language Models into the Shaping of Curricula and Learning Spaces in the Higher Education System
Earnest Anand and Sanjeev Kumar
Introduction
Digital Revolution in the Education System
Large Language Models: Future of Higher Education
Large Language Models in Designing the Curriculum
Large Language Models in Pedagogical Transformation
Digital Twin Classrooms and LLM Integration in Intelligent Campuses
Issues of the Use of Large Language Models in Education
Conclusion
References
14. Large Language Models for Medical Diagnosis and Decision Support
Rohit Kumar Shakya, Sandeep Chouhan and Deepika Ghai
14.1 Introduction
14.2 Background and Literature Review
14.2.1 Contributions of LLMs in Healthcare
14.2.2 Specialized LLM Models in Healthcare
14.2.3 Research Gaps and Opportunities
14.3 LLMs Architecture for Healthcare
14.3.1 Overview of Transformer Architecture
14.3.2 Anatomy of LLMs
14.3.2.1 Personal Example: MRI Report Analyzer
14.3.3 Architecture of Key Medical LLMs
14.4 Proposed Methodology
14.4.1 Preprocessing of Clinical Data
14.4.2 Domain-Specific Pre-Training
14.4.3 Fine-Tuning with Annotated Data
14.4.4 Multimodal Integration (LLM + Imaging/Signals)
14.4.5 Patient-Centered Output Generation
14.5 Applications of LLMs in Healthcare
14.6 Challenges and Ethical Concerns
14.6.1 Technical Shortcomings
14.6.2 Data Privacy Concerns
14.6.3 Fairness and Representation
14.6.4 Lack of Explainability
14.6.5 Risk of Over-Reliance
14.7 Future Scope
14.7.1 Better Domain Adaptation and Specialized Training
14.7.2 Integration with Multimodal Systems
14.7.3 Stronger Ethical and Transparent Frameworks
14.7.4 On-Device and Offline Capabilities
14.7.5 AI-Augmented Clinical Training
14.8 Conclusion
References
15. Evaluation of LLMs
Manjushree Nayak and Sudikshya Panda
15.1 Introduction
15.2 Motivation
15.3 Taxonomy of Evaluation Dimensions
15.4 Classical Static Benchmarks
15.5 Dynamic/Competitive Leaderboards
15.6 Model Architectures and Training Strategies
15.7 Evaluation Fabrics and Ways
15.8 Limitations and Open Challenges
15.9 Future Directions
15.10 Conclusion
Bibliography
16. Role of Large Language Models in Next‑Generation Wireless Communication
Shakti Raj Chopra
16.1 Introduction
16.2 Wireless Communication Systems: Challenges and Trends
16.3 Model Architecture
16.4 Advantages Over Traditional Methods
16.5 Role of LLMs in Wireless Communication
16.5.1 Channel Modeling and Estimation
16.5.2 Signal Processing
16.5.3 Intelligent Resource Allocation
16.6 Security and Anomaly Detection
16.7 Conclusion
References
17. Retrieval-Augmented Generation (RAG): Techniques, Applications, and Future Directions
Krishan Arora
17.1 Introduction
17.1.1 Overview
17.2 Importance of RAG
17.3 Evolution of RAG
17.4 Architecture and Components
17.4.1 Core Components
17.4.2 Workflow
17.4.3 Fusion Strategies
17.5 Methodologies and Implementation
17.5.1 End-to-End RAG
17.5.2 Modular RAG
17.5.3 Indexing Methods
17.5.4 Knowledge Source Types
17.6 Applications
17.6.1 Conversational AI
17.6.2 Healthcare
17.6.3 Legal Sector
17.6.4 Education
17.6.5 Research and Academia
17.6.6 Enterprise Knowledge Systems
17.6.7 Scientific Discovery
17.7 Evaluation and Benchmarking
17.7.1 Retrieval Metrics
17.7.2 Generation Metrics
17.7.3 Human-Centric Evaluation
17.7.4 Benchmark Datasets
17.8 Challenges
17.8.1 Hallucination Persistence
17.8.2 Latency and Infrastructure
17.8.3 Knowledge Obsolescence
17.8.4 Data Privacy and Compliance
17.8.5 Interpretability and Transparency
17.9 Future Directions
17.9.1 Multimodal RAG
17.9.2 Personalized RAG
17.9.3 Federated RAG
17.9.4 Self-Learning RAG
17.9.5 Explainable RAG
17.10 Case Studies
17.10.1 Healthcare Clinical Assistant
17.10.2 Legal Research Platform
17.10.3 E-Commerce Support System
Conclusion
References
Index

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