This Post Has 4 Comments

1. Packt Publishing Invited Editorial Book Review: Dr. Yogesh Malhotra, Global Risk Management Network
   Inspired by AI-Genetic Algorithms pioneer, Professor Dr. John Holland, then at the Santa Fe Institute in 1995, we published the first known AI journal paper on ‘Data is Profoundly Dumb’ pioneering Meaning-Aware Human-Centered AI in 2000, followed by the first journal paper on Real Time Enterprise (RTE) business models in 2005. In the context of Artificial Intelligence and Generative Artificial Intelligence, RAG, i.e., Retrieval-Augmented Generation represents the latest focus on “Right Data” given your specific Chosen ‘Context’, beyond ‘Big Data’ and ‘Small Data’.Retrieval-Augmented-Generation, i.e., RAG amounts to ‘Augmentation’ of your AI- and GenAI- driven Decision-Making process by using Real Time Data ‘Generation’ and ‘Retrieval’ from selected Data Sources and Data Generating Processes. RAG prioritizes using the most current data for decision-making. This is crucial when historical data might not accurately reflect the present or future, and thus ensures that AI systems are working with up-to-date information. More importantly, you need to ensure that your Generated Data also focuses on the ‘Context-Specific’ [‘Time-‘, ‘Space-‘, ‘Entity-‘, etc. “Specific”] information relevant to the specific entity such as the organization or institution that is of your central focus. That explains the subtitle of the book: Enhance generative AI systems by integrating “internal” data with large language models using RAG. With the ‘WHY’ is RAG so important for most Critical Decisions enabled by AI and GenAI established above, the reader will find this specific Hands-On Applied Workbook on RAG titled “Unlocking Data with Generative AI and RAG” authored by Keith Bourne most useful in developing above applied intuitive understanding for real-world practical AI and GenAI applications.As a comprehensive guide for those looking to harness the power of RAG, I find this book a crucial resource for practitioners, researchers, and academics eager to implement RAG effectively. The book covers the core concepts of RAG, including indexing, retrieval, and generation. It provides several complete code labs including one that walks through the implementation of a RAG pipeline using Python, LangChain, and Chroma. It also goes into more advanced techniques like prompt engineering, query expansion, and multi-modal RAG (MM-RAG). The book also discusses the importance of evaluating RAG pipelines, and provides guidance on how to do so effectively. This is a practical, hands-on guide that will be valuable to anyone looking to build RAG applications. It emphasizes real-world coding examples, case studies, and strategies for effective implementation. The book also includes information on setting up a development environment with Jupyter notebooks, and using tools like LangChain, Chroma DB, and OpenAI’s APIs.In essence, as the book will help one recognize, RAG enables AI systems to go beyond their original training data by incorporating real-time information from selected sources, thus improving the relevance and accuracy of their outputs. This process is crucial for making effective decisions in ‘dynamic environments’ that are the central focus of our 30-year R&D leading Dynamic and Adversarial Uncertainty, Time-Space Complexity and Risk Management Practices recognized for global impact among Artificial Intelligence and Quant-Finance Nobel laureates.- Dr.-Eng.-Prof. Yogesh Malhotra ‘Yogi’, Chairman, New York Venture Capital firm Global Risk Management Network, LLC and Founder of AWS Quantum Valley Network – Building Quantum Minds for Managing Quantum Uncertainty and Time-Space Complexity:Our Latest Google AI Podcasts on YouTube: Future Proof Your Career Beyond AI-GenAI–We Create the Digital Future™. You Can Too! Let’s Show You How!™ We Build Quantum Minds for Quantum Uncertainty™ Beyond GenAI–We Create the Digital Future™. You Can Too! Let’s Show You How!AIMLExchange™: BRINT™: C4I-Cyber™Know-Build-Monetize Networks™New York State: “Join Dr. Yogi Malhotra to get up to speed on Cloud Technology.”US Air Force-Air Force Research Lab Ventures: “Do Something Epic: Save the World™”:–

   Reply

2. Great tool for your AI workbench
   Companies and product owners frequently address LLMs, AI, and chatbots as if they are all the same thing and miss the nuance to drive consistent growth. If you are looking for that marketable tweak, “Unlocking Data with Generative AI and RAG” (Packt, 2025) by Keith Bourne could be your secret key. The book is divided into three sections: an introduction, describing the RAG components, and implementing advanced solutions. Each section includes extensive code work with labs well documented through a public GitHub, enabling trial and error learning. If you are developing with LLMs, integrating AI into practices, or just want a better sense of where the market is headed, this is an excellent step.Most times, I’d jump right into the first section, but it is important to make sure we are all on the same page. Retrieval Augmented Generation (RAG) matches generative AIs with LLMs across internal company data. In short, this takes an external LLM, moves it into local data, and then trains with a relevant knowledge base. For example, I want to know how our company handled a particular problem and don’t care about the wider aspects. RAG generates more accurate responses, reduces the instances of hallucinations when AI suggests a low probability and fictitious answer and helps generative systems create the right answers.The first section starts with those basics, introducing the RAG concept, demonstrating how the labs well be used, and detailing the basic components. Similar to many approaches, the book uses Jupyter Notebook in combination with LangChain to build practical essentials. One of the best practical examples is using RAG to produce a curated learning path with generative AI, ensuring students focus on what is needed instead of a solution for the lowest common denominator. The section concludes with a component review and some exciting security ideas for challenging an RAG system with red teams and blue teams.From those basics, the book rapidly digs into how to set up and coordinate your own RAG systems with multiple code labs throughout the second section. Many sections are abbreviated in the book but whole samples appear in the online GitHub applications. There are two great chapters and vectors, and vector stores, and their integration throughout the RAG process. These successfully break down the very technical into easy-to-read interpretations, showing exactly how an LLM with RAG uses tokenization, chunking, and semantic interactions to deliver the right answer at the right point. The author prefers LangChain as the application but demonstrates some external models and metrics to help evaluate how well your AI solutions are working.The final section delivers advanced techniques using LangGraph and some prompt engineering solutions. Remember, it is never good enough to have just a smart AI if you cannot manage to ask the right questions. The prompt engineering explores how to ask, and how to employ smart agents that can fill in data between the user question and the desired LLM. Roles and qualifications become very important here, for example, phrasing the prompt as arriving from a product owner with minimal technical experience or as a software developer with thirty years in the field when looking for a specific technical answer.Overall, , “Unlocking Data with Generative AI and RAG” excels at exactly what it says it will do, brining RAG into the individual toolkit, demonstrating multiple methods, and showing well-coded samples capable of being endlessly adjusted. The Code Labs are well thought out, with multiple samples appearing in each chapter. These link the GitHub to the demonstrated items, showing exactly how to fit the specified RAG models into an existing LLM, convert libraries and packages, and implement generative AI to achieve the needed solutions. If you are working with any aspect of AI, ML, or LLMs, this book will be a welcome addition to your toolkit.

   Reply

3. A Perfect Blend of Theory and Practice
   What struck me most about this book is how well it balances foundational knowledge with actionable insights. Bourne takes the reader on a journey through the intricacies of Generative AI and Retrieval-Augmented Generation (RAG), breaking down complex concepts into easily digestible explanations. The examples and case studies are not only relevant but also inspiring, showcasing how these technologies are already making a difference in fields like healthcare, finance, and customer service.The book shines when discussing practical use cases. From building intelligent chatbots to leveraging RAG for more accurate insights from large datasets, Bourne provides step-by-step guidance that’s both technical and strategic.The emphasis on RAG was particularly exciting for me. The way Bourne explains its role in enhancing AI’s ability to retrieve and generate contextually relevant information opened my eyes to its potential in solving real-world problems. The hands-on examples helped bridge the gap between theory and implementation.This book isn’t just about understanding the technologies—it’s about learning to apply them effectively. Keith Bourne provides the tools and insights needed to unlock the value hidden in data using cutting-edge AI techniques. Whether you’re a data scientist, a business leader, or simply curious about the future of AI, this book is a must-read.

   Reply

4. An excellent resource for understanding how Generative AI and RAG can unlock new opportunities in data utilization.

   Reply