Keith Bourne

Unlocking Data with Generative AI and RAG (eBook, ePUB)

Enhance generative AI systems by integrating internal data with large language models using RAG

• Format: ePub

Produktbeschreibung

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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Produktdetails

• Verlag: Packt Publishing
• Seitenzahl: 346
• Erscheinungstermin: 27. September 2024
• Englisch
• ISBN-13: 9781835887912
• Artikelnr.: 71793022

Autorenporträt

Keith Bourne is a senior Generative AI data scientist at Johnson & Johnson. He has over a decade of experience in machine learning and AI working across diverse projects in companies that range in size from start-ups to Fortune 500 companies. With an MBA from Babson College and a master's in applied data science from the University of Michigan, he has developed several sophisticated modular Generative AI platforms from the ground up, using numerous advanced techniques, including RAG, AI agents, and foundational model fine-tuning. Keith seeks to share his knowledge with a broader audience, aiming to demystify the complexities of RAG for organizations looking to leverage this promising technology.