Unlocking Data with Generative AI and RAG: Enhance generative AI systems by integrating internal data with large language models using RAG
Author:by Keith Bourne (Author)
ASIN:B0DCZF44C9
Publisher finelybook 出版社:Packt Publishing
Edition 版本:N/A
Publication Date 出版日期:2024-09-27
Language 语言:English
Print Length 页数:346pages
ISBN-10:1835887910
ISBN-13:9781835887905
Book Description
Review
“This book is an invaluable resource for software and application developers eager to start building LLM applications. It stands out as one of the most state-of-the-art guides available, covering the latest frameworks and libraries, such as LangChain and Ragas. Whether you’re interested in prototyping new ideas or scaling LLM applications to production, the book provides both theoretical foundations and practical advice on creating robust, scalable AI systems, including emerging concepts such as Retrieval-Augmented Generation (RAG) and agentic workflows.
I am confident that this book will become a go-to resource for anyone looking to harness the power of Generative AI. Keith’s clear, insightful guidance will not only help you understand the potential of these technologies but also empower you to build the next generation of intelligent applications.”
Shahul Es, Co-founder and CTO at Ragas.io
About the Author
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.
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