🤖 Unlocking the Value of AI: Why RAG Systems Struggle And What We Can Do About

AI is evolving fast and with it, new methods for making systems more accurate, reliable, and explainable. One of the most promising techniques is Retrieval-Augmented Generation (RAG), a framework that allows AI models to “look things up” before generating answers. But beneath the surface, even these advanced systems can fall short.
In this executive-friendly session, we’ll unpack what RAG is, why it's being adopted by leading AI teams, and more importantly, where it still breaks down. You'll leave with a clearer understanding of how to improve your AI initiatives, even if you’re not an ML engineer.

***

### 💡 You’ll Learn:

• What RAG is and why it matters for business-critical AI
• Where RAG systems fail: inaccurate outputs, missed context, or confusing results
• Why “feeding AI more documents” doesn’t guarantee better answers
• Key questions to ask your data or product teams to uncover hidden risks
• Strategic techniques for improving your AI reasoning capabilities, accuracy, and trust

***

### 🧠 Who Should Attend?

• C-level executives and VPs exploring AI adoption
• Project Managers, Heads of Product, Innovation, or Strategy
• Data and Analytics Leaders
• Anyone tasked with investing or integrating generative AI solutions
• RAG Users

***

The conversation will be high-level and jargon-free, with plenty of time for Q&A.