As Large Language Models (LLMs) continue to evolve, the need for more context-aware, accurate, and scalable AI solutions has never been greater. Retrieval-Augmented Generation (RAG) bridges the gap between static models and real-world knowledge by dynamically retrieving relevant data to enhance responses. But what if we take it a step further?

In this session, we’ll explore how RAG and AI agents work together to enable intelligent, context-driven decision-making at scale on Azure Databricks. We’ll dive into:
- The fundamentals of RAG and its role in enhancing LLMs
- How AI agents orchestrate workflows, automate tasks, and refine responses
- Implementing RAG pipelines and agents efficiently on Databricks
- Real-world use cases and best practices for enterprise AI applications
Join us to discover how Databricks supercharges RAG and AI agents, unlocking new possibilities for scalable, intelligent, and context-aware AI solutions