Many AI applications suffer from inefficient data processing and outdated responses, leading to poor performance and user dissatisfaction.
Optimizing AI performance is crucial to ensure that systems are both accurate and efficient, meeting the growing demands for real-time, relevant responses in professional environments.

This session will explore the integration of Retrieval-Augmented Generation (RAG) with Azure AI, focusing on practical strategies like effective vectorization, prompt engineering, and leveraging cached responses to enhance AI performance. We will also dive into performance measurement techniques, explaining why these metrics are vital for continuous improvement in AI systems. By the end of this session, participants will understand how to apply these optimizations to create more dynamic, responsive, and efficient AI applications using Azure Machine Learning.

This approach improves AI interaction quality and ensures systems are scalable and responsive to the evolving needs of users and industries.