Exploring Retrieval-Augmented Generation (RAG) in AI Applications
This session will explore Retrieval-Augmented Generation (RAG), a cutting-edge AI approach that combines large language models with enterprise data retrieval for more accurate and grounded outputs. Attendees will gain a practical understanding of how RAG works, where it can be applied, and key considerations for implementation in real-world business settings.
- 12:00 PM
Please login or sign up for a UWEBC Member Account to register for or join this event.
Description
Join us for an engaging session of our AI Special Interest Group as we dive into the topic of Retrieval-Augmented Generation (RAG) — an architecture that combines the strengths of traditional information retrieval with the generative power of large language models.
RAG is gaining momentum as organizations seek to harness generative AI in ways that are accurate, explainable, and grounded in trusted enterprise data. During this session, we’ll explore:
- What RAG is and how it works
- Use cases across industries including customer support, knowledge management, compliance, and internal productivity tools
- Key considerations for implementing RAG in enterprise environments (e.g., data pipelines, retrieval sources, governance)
- Open discussion on challenges, success stories, and lessons learned
Whether you’re just beginning to explore RAG or already experimenting with it in your organization, this is a great opportunity to learn from peers, ask questions, and share your experiences.
Add this event to your calendar
Please note that adding the event to your calendar is not the same as registering. Please also use the button above to register for this event.
Agenda Items
| 10:00 AM | Welcome & Opening Remarks |
| 10:20 AM | Extracting Knowledge from Corpora at UW-Madison with AI-powered RAG systems |
| 11:50 AM | Wrap Up & Closing Remarks |
| 12:00 PM | Adjourn |
Additional Information
Location: ZoomContact: Events Team, events@uwebc.wisc.edu