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1. RAG-Driven Gen AI: A Practical Guide to Advanced AI Systems
   Denis Rothman’s “RAG-Driven Gen AI” offers a comprehensive exploration of Retrieval-Augmented Generation systems, addressing a critical need in the rapidly evolving field of artificial intelligence. This book stands out for its practical approach, bridging the gap between theoretical concepts and real-world applications.Rothman’s writing style is accessible yet thorough, guiding readers from foundational principles to advanced implementations of RAG systems. The book’s structure feels well-considered, allowing readers to build their understanding progressively. While it assumes some prior knowledge of machine learning and Python, making it less suitable for complete beginners, it offers valuable insights for software engineers, developers, and data scientists looking to expand their AI toolkit.One of the book’s strengths lies in its diverse range of practical examples. By covering applications from drone technology to customer retention, Rothman effectively demonstrates the versatility of RAG systems. The chapter on multimodal RAG for drone technology is particularly intriguing, opening up new possibilities that many readers might not have previously considered.A standout feature is the book’s attention to often-overlooked aspects of AI development, such as software versioning and package management. Rothman’s detailed guidance on version control and dependency management addresses real challenges faced by practitioners, potentially saving readers significant time and frustration.The hands-on approach, complete with projects and source code, transforms the book from a mere reference into a practical learning tool. Rothman doesn’t shy away from discussing performance optimization and cost management – crucial considerations for implementing AI solutions in production environments.However, readers should be aware that the rapid pace of AI advancement may necessitate supplementing this book with current research and developments. Some cutting-edge concepts discussed may evolve quickly.”RAG-Driven Gen AI” serves as a valuable resource for those looking to understand and implement RAG systems. While it may not be the only book you’ll need on the subject, it provides a solid foundation and practical insights that many readers will find useful. Rothman’s work effectively captures the current state of RAG technology while offering guidance that should remain relevant as the field continues to evolve.For professionals aiming to leverage the power of RAG systems or enhance their AI capabilities, this book is a worthwhile addition to their technical library. It offers a balanced mix of theoretical understanding and practical application, making it a useful companion for those navigating the complex landscape of modern AI development.

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2. The book that separates signal from noise in the rapidly evolving AI tech scene
   Rothman’s RAG-Driven Gen AI is a tour de force in the rapidly evolving field of AI. Even for folks who are deeply immersed in the fields of data science and ML, it’s non-trivial these days to stay on top of the rapid pace of innovation and separating out what matters from the inevitable hype and vaporware. I found this book to be an invaluable resource that articulates how latest Gen AI tools come together to solve real life problems, bridging the gap between theory and practical application seamlessly.Rothman’s approach to explaining RAG is both comprehensive and accessible. He starts with the fundamentals, gradually building up to more complex implementations, which I found particularly helpful (though it must be said, that some prior background in ML is assumed). The book’s structure, moving from basic concepts to advanced applications across various chapters, allows readers to grow their understanding organically.What sets this book apart is its hands-on approach. Rothman doesn’t just tell you about RAG; he shows you how to build it from the ground up. The practical examples using popular frameworks like LlamaIndex, Pinecone, and Deep Lake were eye-opening. I especially appreciated the detailed walkthrough of creating a RAG framework from scratch – it’s these kinds of insights that are often missing from more theoretical texts. The practical examples chosen, spanning drone technology to customer retention and knowledge-graph systems, makes this book incredibly versatile and showcases the broad use cases for RAG and Gen AI. The chapter on multimodal modular RAG for drone technology was a standout for me. It’s fascinating to see how RAG can be applied to combine textual and visual data, opening up new possibilities for AI applications in fields I hadn’t even considered before. I’m definitely inspired to do more with Gen AI!I was also impressed by the attention given to performance optimization and cost management. In the real world, these are crucial considerations that often get overlooked in academic discussions of AI. Rothman’s practical advice on when to fine-tune models and how to improve retrieval speed with knowledge graphs is worth its weight in gold for any practitioner looking to implement these systems in a production environment.IMO this isn’t just a book, but rather a roadmap for the future of AI development. Rothman has once again demonstrated his knack for demystifying complex concepts and providing actionable insights. Whether you’re looking to enhance your AI’s accuracy, manage costs more effectively, or push the boundaries of what’s possible with generative AI, this book has something valuable to offer. It’s earned a permanent spot on my reference shelf, and I have no doubt I’ll be returning to it frequently as I continue to work with and implement RAG-driven systems.

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3. Confusing
   I have read previous excellene books from this author. However this one is confusing, not very good diagrams. There are lots of good papers and chapters on other books on this topic, but this book is not good.

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4. A must-have guide to designing RAG-based Generative AI applications
   The book provides a comprehensive guide to building advanced multimodal AI systems with Retrieved Augmented Generation (RAG).It offers practical insights into designing, building, managing and optimising pipelines that include large language models (LLMs), vector databases, knowledge graphs.Few of the key topics include techniques for improving output accuracy of the combined similarity search and prompting LLMs, optimising retrieval from vector databases, improving all the steps of ingesting documents in vector storages: format conversion, chunking, indexing, ranking.Thought the chapters dedicated to various implementation of the RAG systems, from naive to advanced and optimised, it covers as well hands-on implementations examples using various technologies: LlamaIndex, Deep Lake, Pinecone, ChromaDB. Models from HuggingFace and OpenAI are used for these applications.The book is useful for data scientists, AI engineers, machine learning professionals, and MLOps practitioners, as well as software developers, product managers, and project leads aiming to create robust, context-aware AI systems for diverse applications.

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5. One of the most useful books I have on the subject. Very informative, easy to follow and practical examples. If only I’d had this book from the start it would have saved a lot of time.

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6. RAG being in the forefront of Gen AI LLM models is a highly sought after skill or knowledge to have.This book covers the theory part of RAG, vectorization, Vector databases.Yet what I found most fascinating was the code snippets, applications that you can directly use in your GenAI application with a bit of modification.Just one advice be clear on Transformer and language models before learning RAG.For this I would recommend Denis’s other book Transformers for NLP.

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7. The book has at its fundamental level a useful framework for thinking about this emerging technology, but the presentation in written English is incredibly poor. The choice of words, phrases and jargon and the way these are mixed up throughout the book makes it incredibly difficult to interpret. The lack of precision in language and the mixing up of terminology means you must read and then re-read whole sections to get a grasp on the objects under discussion, their relationships to each other and the overall message. The skill of the writing is so poor that the burden on the reader is increased to really unreasonable levels.

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8. book apperience is very bad , books looked like old and used

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