Retrieval-Augmented Generation (RAG) enhances the accuracy, reliability and relevancy of AI’s responses by ensuring that your large language model (LLMs) chatbots have access to the most recent and relevant information from enterprise data sources. RAG can be crucial to delivering a generative AI application that is trusted by your users, as well as delivering responses in a fast and cost-effective manner.

In this session, we’ll explore how Azure OpenAI, Azure AI Search (formerly Cognitive Search), and other Azure services can be used to create a comprehensive solution for enterprise-ready RAG applications. You will learn how to leverage the Azure platform to deploy GenAI at scale, using services like Entra ID for identity and access management to ensure secure access to data, and Application Insights to track when things go wrong.

We’ll demonstrate different approaches to building and deploying RAG applications, including how to load-test and evaluate RAG performance through automated testing, as well as data storage options and front-end frameworks to enhance user experience. We will also share real-world use cases and what we have learned from helping businesses understand Azure’s GenAI capabilities and weaknesses, with a shared goal to increase productivity and accurate knowledge management.

Finally, we'll discuss strategies for driving adoption of GenAI. From deployment to user onboarding, you’ll leave with actionable insights to ensure your AI projects have a lasting impact across your organisation. Join us to explore how to effectively use Azure’s GenAI tools and RAG deployment approaches to create robust solutions that are scalable, secure, and user-friendly.

This is a joint session on building well-architected generative AI solutions in Azure, and how to ensure security, scalability and optimal user experience. We will demo different approaches and options when delivering these solutions.