RAG Isn't a Silver Bullet - The Reality of Making GenAl Reliable

Everywhere you look, teams are chasing reliability in GenAI.The promised solution: RAG — Retrieval-Augmented Generation.Ground the model in your company’s data, and it’ll stop hallucinating… right?Not quite.As Hubert Duan, Principal at Microsoft AI, and I discuss — RAG introduces its own complexities: retrieval quality, data freshness, latency, and failure modes that only surface at scale.In this GovAI.fm conversation, we go beyond the hype to explore what it really takes to make RAG work in production:🔍 What RAG really is (and how it’s often misunderstood)⚠️ The list of failure modes that teams encounter when scaling RAG🏗 How to design resilient, scalable RAG architectures🚀 Why RAG alone won’t fix reliability — and what’s next, from hard scaling lessons to Agent-to-Agent (A2A) intelligenceWe also talk about Hubert’s public sector successes, including projects like AI for wildfire forecasting in Alberta — and how building AI for good causes can help attract and retain talented public sector AI professionals.Finally, Hubert shares his perspective on the future of AI, from RAG’s evolution to the emerging world of autonomous, agent-to-agent systems.If you’ve ever wondered why your GenAI prototype struggles in production — or what’s coming next — this episode connects the dots.