Retrieval-Augmented Generation (RAG) has become a cornerstone for enterprise AI, enabling models to ground outputs in proprietary knowledge and deliver trustworthy results. This panel will break down the critical components of a RAG system, from data ingestion and indexing, to vector databases, embedding models, retrieval pipelines, and response optimization layers. Panelists will explore the evolving vendor landscape, comparing open-source and commercial RAG platforms, as well as strategies for integrating them into enterprise environments. Expect discussion on cost, latency, scalability, and governance trade-offs, plus real-world use cases where RAG has unlocked measurable business value. Attendees will walk away with a blueprint of the cutting-edge RAG tools and strategies needed to move beyond experimentation and build production-grade AI applications.
