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