Retrieval-Augmented Generation (RAG) is emerging as a key enabler of enterprise-grade generative AI, bridging the gap between static models and dynamic, real-time data. This use- case oriented panel will explore how organizations are using RAG to enhance accuracy, improve trust, and deliver meaningful business outcomes. From customer support and internal knowledge access to legal, finance, and operations, hear how leading enterprises are implementing RAG architectures to supercharge their generative AI applications. Learn about best practices, tooling options, and performance measurement frameworks to turn your unstructured data into strategic advantage.
