Description
Generative AI has transformed industries, enabling the creation of human-like text, images, and code. However, traditional generative models often suffer from inaccuracies and hallucinations due to their reliance on pre-trained data. Retrieval-Augmented Generation (RAG) addresses this limitation by integrating retrieval mechanisms, enhancing the quality, accuracy, and relevance of generated content.
Beyond the foundational aspects, this book delves into the core mechanisms that make RAG more effective than traditional generative models. It covers advanced embedding techniques for efficient knowledge retrieval, vector database optimization, and fine-tuning transformer models to dynamically fetch and incorporate external knowledge into generated responses. Readers will also explore dense retrieval methods, indexing strategies, and real-time query optimization to enhance generative model performance.
Additionally, the book explores the synergy between RAG and Large Language Models (LLMs), discussing how hybrid architectures can be designed for improved accuracy, lower computational costs, and reduced model hallucinations. Through case studies and hands-on examples, readers will gain practical insights into deploying RAG-based AI systems at scale, optimizing inference speeds, and ensuring data relevance in diverse application domains. The final chapters will present emerging trends in retrieval-based architectures, multimodal AI integration, and the potential role of RAG in decentralized and federated learning environments.
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.
