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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

Vector search options

In basic terms, vector search is the process of finding the vectors most similar to the query vector within the vector store. The ability to quickly identify relevant vectors is crucial for the system’s overall performance, as it determines which pieces of information will be used by the LLM for generating responses. This component bridges the gap between the raw user query and the data-rich inputs needed for high-quality generation. There are numerous offerings and numerous types of offerings in the marketplace that you can use to conduct your vector search. We have talked a lot so far about the components and concepts that make a good vector search. You can apply that knowledge to selecting the best vector search option for your specific project needs. Services are evolving quickly with new start-ups emerging every day, so it is worth your effort to do some deep research before deciding on an option. In the next few subsections, we will look at the few...

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Unlocking Data with Generative AI and RAG
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