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Unlocking Data with Generative AI and RAG

Unlocking Data with Generative AI and RAG

By : Keith Bourne
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Unlocking Data with Generative AI and RAG

Unlocking Data with Generative AI and RAG

By: Keith Bourne

Overview of this book

Generative AI is helping organizations tap into their data in new ways, with RAG combining the strengths of 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. *Email sign-up and proof of purchase required
Table of Contents (20 chapters)
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Part 1 – Introduction to Retrieval-Augmented Generation (RAG)
7
Part 2 – Components of RAG
14
Part 3 – Implementing Advanced RAG

Additional evaluation techniques

Ragas is just one of many evaluation tools and techniques available to evaluate your RAG system. This is not an exhaustive list, but in the following subsections, we will discuss some of the more popular techniques you can use to evaluate the performance of your RAG system, once you have obtained or generated ground-truth data.

Bilingual Evaluation Understudy (BLEU)

BLEU measures the overlap of n-grams between the generated response and the ground-truth response. It provides a score indicating the similarity between the two. In the context of RAG, BLEU can be used to evaluate the quality of the generated answers by comparing them to the ground-truth answers. By calculating the n-gram overlap, BLEU assesses how closely the generated answers match the reference answers in terms of word choice and phrasing. However, it’s important to note that BLEU is more focused on surface-level similarity and may not capture the semantic meaning or relevance...

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Unlocking Data with Generative AI and RAG
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