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    Unlocking Data with Generative AI and RAG. Enhance generative AI systems by integrating internal data with large language models using RAG Keith Bourne, Shahul Es

    (ebook) (audiobook) (audiobook) Książka w języku angielskim
    Unlocking Data with Generative AI and RAG. Enhance generative AI systems by integrating internal data with large language models using RAG Keith Bourne, Shahul Es - okladka książki

    Unlocking Data with Generative AI and RAG. Enhance generative AI systems by integrating internal data with large language models using RAG Keith Bourne, Shahul Es - okladka książki

    Unlocking Data with Generative AI and RAG. Enhance generative AI systems by integrating internal data with large language models using RAG Keith Bourne, Shahul Es - audiobook MP3

    Unlocking Data with Generative AI and RAG. Enhance generative AI systems by integrating internal data with large language models using RAG Keith Bourne, Shahul Es - audiobook CD

    Autorzy:
    Keith Bourne, Shahul Es
    Serie wydawnicze:
    Hands-on
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    346
    Dostępne formaty:
         PDF
         ePub
    Generative AI is helping organizations tap into their data in new ways, with retrieval-augmented generation (RAG) combining the strengths of large language models (LLMs) with internal data for more intelligent and relevant AI applications. The author harnesses his decade of ML experience in this book to equip you with the strategic insights and technical expertise needed when using RAG to drive transformative outcomes.
    The book explores RAG’s role in enhancing organizational operations by blending theoretical foundations with practical techniques. You’ll work with detailed coding examples using tools such as LangChain and Chroma’s vector database to gain hands-on experience in integrating RAG into AI systems. The chapters contain real-world case studies and sample applications that highlight RAG’s diverse use cases, from search engines to chatbots. You’ll learn proven methods for managing vector databases, optimizing data retrieval, effective prompt engineering, and quantitatively evaluating performance. The book also takes you through advanced integrations of RAG with cutting-edge AI agents and emerging non-LLM technologies.
    By the end of this book, you’ll be able to successfully deploy RAG in business settings, address common challenges, and push the boundaries of what’s possible with this revolutionary AI technique.

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    O autorze książki

    Keith is a Senior Generative AI Data Scientist at Johnson & Johnson, leveraging his decade of experience in machine learning. With an MBA from Babson College and a Master of Applied Data Science from the University of Michigan, Keith has made significant contributions to healthcare innovation through his expertise in generative AI, particularly in developing a sophisticated generative AI platform incorporating Retrieval-Augmented Generation (RAG) and other advanced techniques. Keith has worked with a diverse set of clients including University of Michigan Healthcare, NFL, NOAA, Weather Channel, Becton Dickinson, Toyota, and Little Caesars.
    Originally from Chagrin Falls, OH, Keith resides in Ann Arbor, MI with his wife and three daughters.

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