In this session will demystify Retrieval Augmented Generation (RAG) models, which represent a pivotal advancement in AI by combining retrieval mechanisms with generative models to produce more accurate and contextually relevant outputs. Starting with the foundational concepts of RAG, including the roles of the retriever and generator, we'll explore how these models outperform traditional approaches. We'll give a walkthrough on the process of training RAG models & introduce the concept of Multi-Modal RAG, which integrates both text and image data, opening new avenues for AI applications and use-cases across various sectors and industries and end-to-end development to deployment process and public Cloud platform Generative AI service for Azure and Oracle.

We will also look at some of the Industries Best Practices, Methodology and also talk on some of the Industry wide Cyber Security Frameworks including NIST, ISO and Model Risk Management.