RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI (De Gruyter Textbook)

This book provides a deep dive into the foundations of RAG, covering transformer architectures, vector similarity search, and the integration of retrieval techniques into generative models. Readers will learn to implement RAG using open-source 
tools, explore real-world applications in chatbots, legal and medical document retrieval, and AI-assisted research, and address challenges like bias and misinformation. 

Saloni Garg is a Senior Machine Learning Engineer based in Mountain View, California. With deep expertise in scalable ML systems and infrastructure, she has made impactful contributions to open-source projects including Ray and PyTorch. A passionate technologist and community advocate, Saloni has spoken at 18+ international conferences on topics ranging from distributed training to AI safety.
Her work has been recognized globally, she is a recipient of the prestigious Women in Open Source Award by Red Hat and was honored as a Google Venkat Panchapakesan Scholar for her commitment to using technology for social good.
Saloni is driven by a mission to make advanced AI more accessible, explainable, and responsible. This book is her latest effort to bridge cutting-edge AI with real-world applications, empowering engineers, researchers, and enthusiasts to rethink the future of reasoning systems.

Amit Sagtani is an AI practitioner and software architect based in Berlin, Germany, with a deep passion for bridging the gap between research and practical applications in artificial intelligence. With years of experience designing and deploying scalable, high-performance systems, he specializes in building innovative solutions powered by retrieval-augmented generation (RAG) and generative AI technologies. Throughout his career, Amit has worked on transformative projects across domains such as healthcare, cloud computing, and knowledge management. His expertise lies in processing large datasets and developing applications that connect custom knowledge repositories with large language models (LLMs) using RAG, enabling organizations to extract actionable insights from complex information. A strong advocate for open-source
technology and collaboration, he is committed to making advanced AI accessible to a broader audience. Driven by curiosity and a dedication to innovation, His mission to empower developers, researchers, and organizations to harness the full potential of generative AI. When not immersed in writing code, Amit enjoys participating in improv, building open-source communities, playing dodgeball, and sharing knowledge.

Kamal Kant Hiran has over 20 years of experience in academia, research, and administration in Asia, Africa, Europe, and North America. Currently he is working as Research Fellow at the Aalborg University, Copenhagen, Denmark. He has published 30 International books with repute publishers like BPB Publications, IGI Global, De Gruyter from India, Germany, USA, UK. He has made significant contributions to our society's technological transformation. He has several awards to his credit such as International travel grant for attending the IEEE Region 8 Committee Meeting at Warsaw, Poland; International travel grant for Germany from ITS Europe, Passau, Germany; Best Research Paper Award at the University of Gondar, Ethiopia; IEEE Liberia Subsection Founder Award; Gold Medal Award in MTech (Hons.); IEEE Ghana Section Award-Technical and Professional Activity Chair; IEEE Senior Member Recognition, Best IEEE Student Branch Award, Elsevier Reviewer Recognition Award. He has published 75 scientific research papers in SCI/Scopus/Web of Science and IEEE Transactions Journal, conferences, 4 Indian Patents, 1 Australian Patent Grants. He has made several international visits to Denmark, Sweden, Germany, Norway, Ghana, Liberia, Ethiopia, Russia, Dubai, Mexico, and Jordan for research exposures. His research interests focus on Technology Adoption, Artificial Intelligence, Cloud-edge computing, Machine-deep learning and Intelligent IoT.