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.

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Spis treści

Unlocking Data with Generative AI and RAG. Enhance generative AI systems by integrating internal data with large language models using RAG eBook -- spis treści

• 1. What Is Retrieval-Augmented Generation (RAG)
• 2. Code Lab – An Entire RAG Pipeline
• 3. Practical Applications of RAG
• 4. Components of a RAG System
• 5. Managing Security in RAG Applications
• 6. Interfacing with RAG and Gradio
• 7. The Key Role Vectors and Vector Stores Play in RAG
• 8. Similarity Searching with Vectors
• 9. Evaluating RAG Quantitatively and with Visualizations
• 10. Key RAG Components in LangChain
• 11. Using LangChain to Get More from RAG
• 12. Combining RAG with the Power of AI Agents and LangGraph
• 13. Using Prompt Engineering to Improve RAG Efforts
• 14. Advanced RAG-Related Techniques for Improving Results