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What’s really happening inside AI workflows when they break? The common story is that models hallucinate or fail at reasoning — but the reality is more complicated. In this video, I share the inside scoop on the six failure patterns I see across AI use at work: • Why “schema-first prompting” fixes most misunderstood outputs • How to stop the infinite regeneration loop in ChatGPT • What causes planning and confidence illusions in large language models • Where context overload and drift quietly destroy consistency The takeaway: most AI errors aren’t model failures—they’re design errors in how we prompt, plan, and constrain. Subscribe for daily AI strategy and news. For deeper playbooks and analysis: https://natesnewsletter.substack.com/