Retrieval Augmented Generation for Natural Language Processing - Hardcover

Hardcover

ISBN 10: 1394336098 ISBN 13: 9781394336098

Publisher: Wiley-Scrivener, 2026

Synopsis

Master the cutting-edge technology bridging the gap between massive AI capabilities and precise corporate reality with this essential guide to overcoming LLM limitations and deploying secure, domain-specific Retrieval-Augmented Generation solutions across real-world industries.

The natural language processing domain has witnessed remarkable growth due to the availability of diverse, high-volume data and advanced machine-learning techniques, particularly large language models. Large language models trained on massive datasets can perform diverse tasks ranging from machine translation to text generation. However, these models face challenges such as factual inaccuracy, biases in data, and a lack of domain-specific knowledge.

This book explores the Retrieval-Augmented Generation (RAG) spectrum, focusing on current trends, challenges, and applications. It introduces large language models and their capabilities, followed by the issues they face, particularly the lack of domain-specific knowledge. It also covers the fundamentals of retrieval-augmented generation and the process of integrating information retrieval with text generation, explaining how RAG bridges the gap between statistical learning and real-world information repositories.

Different information retrieval techniques, generation models, and evaluation metrics such as BLEU score, ROUGE score, and task-specific metrics used to assess model effectiveness are discussed. The book also addresses critical security and privacy concerns, as well as ethical considerations and policies surrounding retrieval-augmented generation.

Case studies covering knowledge management through summarization techniques, personalized learning in education, and customized customer-service chatbots demonstrate the broad potential of RAG systems. This essential guide provides a deep understanding of this transformative technology and how it is revolutionizing human-machine interaction.

About this title

About the Author

Sachin Minocha, PhD, is an Assistant Professor at Amity University, Uttar Pradesh, India. He holds seven patents and has authored more than 15 publications in conference proceedings, book chapters, and refereed journals. His research interests include machine learning, deep learning, nature-inspired optimization techniques, and hyperspectral imaging.

Malathy Sathyamoorthy, PhD, is an Assistant Professor in the Department of Information Technology at KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India. She has published more than 25 journal articles, 22 conference papers, two patents, one book, and four book chapters. Her research focuses on wireless sensor networks, networking, security, and machine learning.

Rajesh Kumar Dhanaraj, PhD, is a Professor at Symbiosis International University in Pune, India. He has authored or edited more than 50 books on emerging technologies, published more than 115 journal and conference papers, and holds 22 patents. His research interests include machine learning, cyber-physical systems, and wireless sensor networks.

Mayank Kumar Goyal, PhD, is an Associate Professor in the Department of Computer Science and Engineering at Sharda University. He has published more than 60 research papers and articles in international journals and conferences. His research interests include emerging technologies, artificial intelligence, cybersecurity, fintech, innovation, and intellectual property development.

From the Back Cover

Master the cutting-edge technology bridging the gap between massive AI capabilities and precise corporate reality with this essential guide to overcoming LLM limitations and deploying secure, domain-specific Retrieval-Augmented Generation solutions across real-world industries.

The natural language processing domain has witnessed remarkable growth due to the availability of diverse, high-volume data and advanced machine-learning techniques, particularly large language models. Large language models trained on massive datasets can perform diverse tasks ranging from machine translation to text generation. However, these models face challenges such as factual inaccuracy, biases in data, and a lack of domain-specific knowledge.

This book explores the Retrieval-Augmented Generation (RAG) spectrum, focusing on current trends, challenges, and applications. It introduces large language models and their capabilities, followed by the issues they face, particularly the lack of domain-specific knowledge. It also covers the fundamentals of retrieval-augmented generation and the process of integrating information retrieval with text generation, explaining how RAG bridges the gap between statistical learning and real-world information repositories.

Different information retrieval techniques, generation models, and evaluation metrics such as BLEU score, ROUGE score, and task-specific metrics used to assess model effectiveness are discussed. The book also addresses critical security and privacy concerns, as well as ethical considerations and policies surrounding retrieval-augmented generation.

Case studies covering knowledge management through summarization techniques, personalized learning in education, and customized customer-service chatbots demonstrate the broad potential of RAG systems. This essential guide provides a deep understanding of this transformative technology and how it is revolutionizing human-machine interaction.

Edition details

• Publisher: Wiley-Scrivener
• Publication date: 2026
• Language: English
• ISBN 10: 1394336098
• ISBN 13: 9781394336098
• Binding: Hardcover
• Edition number: 1
• Number of pages: 480
• Editor: Minocha Sachin, Sathyamoorthy Malathy, Dhanaraj Rajesh Kumar, Goyal Mayank Kumar