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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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1
Part 1 – Introduction to Retrieval-Augmented Generation (RAG)
7
Part 2 – Components of RAG
14
Part 3 – Implementing Advanced RAG

Summary

In this chapter, we explored RAG and its ability to enhance the capabilities of LLMs by integrating them with an organization’s internal data. We learned how RAG combines the power of LLMs with a company’s private data, enabling the model to utilize information it was not originally trained on, making the LLM’s outputs more relevant and valuable for the specific organization. We also discussed the advantages of RAG, such as improved accuracy and relevance, customization to a company’s domain, flexibility in data sources used, and expansion of the model’s knowledge beyond its original training data. Additionally, we examined the challenges and limitations of RAG, including dependency on data quality, the need for data cleaning, added computational overhead and complexity, and the potential for information overload if not properly filtered.

Midway through this chapter, we defined key vocabulary terms and emphasized the critical importance...

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Tech Concepts
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Programming languages
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Unlocking Data with Generative AI and RAG
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