Episode
How to Trumpify Your Copilot: A Masterclass in Hallucination
- Published
- Jun 7, 2026
- Duration seconds
- 4748
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Summary
Everyone talks about hallucinations as if they're a model problem. They blame GPT-4, Claude, Gemini, or whatever large language model happens to be in the spotlight this week. They tweak prompts, add more tokens, experiment with different temperatures, and hope the problem magically disappears.But what if hallucinations aren't a model problem at all?What if your Copilot is working exactly as designed?In this episode of the M365 FM Podcast, we take a deep dive into the real causes of hallucinations in Microsoft Copilot, Retrieval-Augmented Generation (RAG) systems, enterprise AI deployments, and custom agents. Through a deliberately provocative thought experiment, we explore how organizations accidentally engineer systems that reward confident wrong answers while creating the illusion of governance, compliance, and control.This isn't an episode about prompt tricks. It's an architectural masterclass on why AI systems hallucinate and how poor retrieval, weak governance, bad permissions, noisy data, and flawed orchestration combine to create enterprise-scale misinformation engines. THE MYTH OF THE BROKEN MODEL Most organizations assume hallucinations originate inside the large language model itself.The reality is more uncomfortable.Large Language Models are trained to predict the next token, not to discover truth. Reinforcement Learning from Human Feedback rewards helpfulness, fluency, and confidence. The result is a system optimized to sound correct even when certainty is impossible.In this episode, we explore how benchmark design, human evaluation systems, and model training methodologies unintentionally create incentives that reward plausible answers over accurate answers.The shocking conclusion is that many hallucinations are not bugs. They are the logical outcome of t…