Dive into the world of Large Language Models (LLMs) and learn how to leverage Databricks for advanced AI applications. This session will explore the principles of Retrieval-Augmented Generation (RAG) and multi-stage reasoning workflows with LLMs. Participants will gain insights into vector search strategies, vector databases, and best practices for improving search-retrieval performance. We will also cover the integration of LLMs into complex workflows using tools like LangChain, enabling tasks that require multi-stage reasoning and agent-based interactions.

Learning Objectives:
By the end of this session, participants will be able to:

-Explain vector search strategies and evaluate search results.
-Define the utility and applications of vector databases.
-Implement Retrieval-Augmented Generation workflows using vector search and LLMs.
-Apply LangChain to build complex logic flows with agents for multi-stage reasoning.
-Utilize best practices for enhancing search-retrieval performance and integrating LLMs into larger workflows.