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

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. Spis treści: 1. Introduction to Retrieval Augmented Generation (RAG)2. Practical Applications of RAG3. Components of a RAG system4. Managing Security in RAG Applications5. Interfacing with RAG and Streamlit6. The Key Role Vectors and Vector Databases Play In RAG7. Similarity Searching with Vectors8. Incorporating LLMs into RAG Systems9. Evaluating RAG Quantitatively and With Visualizations10. Combining RAG with the Power of AI Agents11. Utilizing Prompt Engineering to Improve RAG Efforts12. Advanced RAG Related Techniques for Improving Results13. Top Challenges with RAG and How to Address Them14. Going Beyond the LLM O autorze: Keith is a Senior Generative AI Data Scientist at Johnson & Johnson, leveraging his decade of experience in machine learning. With an MBA from Babson College and a Master of Applied Data Science from the University of Michigan, Keith has made significant contributions to healthcare innovation through his expertise in generative AI, particularly in developing a sophisticated generative AI platform incorporating Retrieval-Augmented Generation (RAG) and other advanced techniques. Keith has worked with a diverse set of clients including University of Michigan Healthcare, NFL, NOAA, Weather Channel, Becton Dickinson, Toyota, and Little Caesars.Originally from Chagrin Falls, OH, Keith resides in Ann Arbor, MI with his wife and three daughters.