Databricks Vector Search: What, Why and How (repeated)

This session is repeated.

Building a successful GenAI application requires more than just leveraging LLMs. It's essential to provide the right context for these models via semantic search to ensure fast and accurate responses. Databricks Vector Search is designed with scalability and simplicity in mind, offering a powerful tool for simplifying the complexity of semantic search. It includes data ingestion, embedding generation, and serving "search." This session will equip you with a toolbox to enhance your GenAI applications and speed up deployments to production while adhering to complex governance and compliance requirements. We'll cover the development of a prototypical RAG solution, along with the most common challenges faced in the field and how to overcome them, including:

• Maintaining Index Health 
• Using Effective Chunking Strategies
• Improving Vector Search recall and  precision
• Scaling ingestion
• Improving retrieval time
• Applying Governance