Unlocking Data with Generative AI and RAG. Enhance Generative AI systems by integrating internal data with Large Language Models using RAG

Generative AI is enabling organizations to tap into their data in new ways, driving innovation and strategic advantage. At the forefront is Retrieval-Augmented Generation (RAG), which combines the strengths of Large Language Models (LLMs) with internal data for more intelligent and relevant AI applications.
Blended with theoretical foundations with practical techniques, it explores RAG's role in enhancing organizational operations. Through detailed coding examples using tools like LangChain and Chroma's vector database, you will gain hands-on experience in integrating RAG into AI systems. Real-world case studies and sample applications shed light on RAG's diverse use cases, from search engines to chatbots. You will learn proven methods for managing vector databases, optimizing data retrieval, effective prompt engineering, and quantitatively evaluating performance. The book also delves into advanced integrations of RAG with cutting-edge AI agents and emerging non-LLM technologies.
By the end, you will have the skills to successfully deploy RAG in business settings, address common challenges, and push the boundaries of what's possible with this revolutionary AI technique. Equipped with strategic insights and technical expertise, you will leverage RAG to unlock your data and drive transformative outcomes.