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Key benefits

Implement RAG’s traceable outputs, linking each response to its source document to build reliable multimodal conversational agents

Deliver accurate generative AI models in pipelines integrating RAG, real-time human feedback improvements, and knowledge graphs

Balance cost and performance between dynamic retrieval datasets and fine-tuning static data

Description

RAG-Driven Generative AI provides a roadmap for building effective LLM, computer vision, and generative AI systems that balance performance and costs. This book offers a detailed exploration of RAG and how to design, manage, and control multimodal AI pipelines. By connecting outputs to traceable source documents, RAG improves output accuracy and contextual relevance, offering a dynamic approach to managing large volumes of information. This AI book shows you how to build a RAG framework, providing practical knowledge on vector stores, chunking, indexing, and ranking. You’ll discover techniques to optimize your project’s performance and better understand your data, including using adaptive RAG and human feedback to refine retrieval accuracy, balancing RAG with fine-tuning, implementing dynamic RAG to enhance real-time decision-making, and visualizing complex data with knowledge graphs. You’ll be exposed to a hands-on blend of frameworks like LlamaIndex and Deep Lake, vector databases such as Pinecone and Chroma, and models from Hugging Face and OpenAI. By the end of this book, you will have acquired the skills to implement intelligent solutions, keeping you competitive in fields from production to customer service across any project.

Who is this book for?

This book is ideal for data scientists, AI engineers, machine learning engineers, and MLOps engineers. If you are a solutions architect, software developer, product manager, or project manager looking to enhance the decision-making process of building RAG applications, then you’ll find this book useful.

What you will learn

Scale RAG pipelines to handle large datasets efficiently

Employ techniques that minimize hallucinations and ensure accurate responses

Implement indexing techniques to improve AI accuracy with traceable and transparent outputs

Customize and scale RAG-driven generative AI systems across domains

Find out how to use Deep Lake and Pinecone for efficient and fast data retrieval

Control and build robust generative AI systems grounded in real-world data

Combine text and image data for richer, more informative AI responses

What do you get with Print?

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£37.99

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Unlocking Data with Generative AI and RAG

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Total £ 106.97

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Jorge Deflon Oct 10, 2024

I have been reading this new book on generative artificial intelligence complemented with RAG (Retrieval-Augmented Generation) and I find it quite useful and interesting.LLM models are advanced artificial intelligence systems designed to process and generate human language.They are trained with enormous amounts of text from several sources, to understand and respond coherently to a wide variety of questions and requests, but this also carries the disadvantage that they may not have the most relevant information for an organization, since it was not available when the model was trained, either due to time or confidentiality issues.Retrieval enhanced generation (RAG) is the process of optimizing the output so that it references an personalized knowledge base before generating a response.This allows the GAI to produce more useful and reliable responses to the organization's users.This book is one of the most complete and up-to-date references on how to use RAG techniques to improve the responses that GAI tools provide to organizational users.The book contains many examples on how use the different types of RAG, including the necessary code to incorporate it into your projects quickly and efficiently.Highly recommended for all practitioners, developers, and students of the topic of generative artificial intelligence.

Amazon Verified review

Subhayan Roy Oct 11, 2024

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.

Gabe Rigall Oct 02, 2024

As in previous offerings, Denis Rothman goes above and beyond to provide clear, comprehensible insight into the concepts behind and real-world applications of his book's subject. This book provides both overviews and step-by-step instructions on how and when to implement RAG-driven AI in your daily workflow. I particularly appreciated his attention to detail when it came to things like version control/install order to avoid package collisions for certain Python packages. It's those little details that can foil even the hardiest of data scientists.That being said, this book is not for the uninitiated novice. It requires more than a basic understanding of machine learning workflows and generative AI. I would recommend this to any data scientist looking to up their Generative AI game.

Colin Skow Oct 07, 2024

RAG-Driven Generative AI covers the basics very well in a way that is accessible to anyone who knows rudimentary Python and is familiar with language model prompting. It then quickly transitions to intermediate and advanced topics at the cutting edge of the field.I am impressed with the way that book introduces new concepts and explains them in a way that is easy to understand complete with ample diagrams and flowcharts. Each major concept is accompanied by a practical real-world project including complete setup instructions and full source code.The technology behind RAG is so new that there honestly aren't a lot of good learning resources out there. Denis Rothman's latest book does a stellar job of making RAG easy to understand and arms the reader with all of the necessary tools for practical application. The material is well-organized, easy to follow, and once you know the basics you can jump to whatever topics interest you.I'd definitely recommend this book to any software engineer, developer, or data scientist who wants to gain practical experience and add RAG expertise to their resume. Mr. Rothman's tech books consistently exceed my expectations and become go-to references in my collection.(Packt provided me a review copy)

Thomas M. Oct 06, 2024

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.

RAG-Driven Generative AI: Build custom retrieval augmented generation pipelines with LlamaIndex, Deep Lake, and Pinecone

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