Session

From Retrieval to Reasoning: RAG & AI Agents on Databricks

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

Ashwini Mahendiran

Senior Software Engineer - AI

Coimbatore, India

Actions

Please note that Sessionize is not responsible for the accuracy or validity of the data provided by speakers. If you suspect this profile to be fake or spam, please let us know.

Jump to top