Stop blaming model quality when your planning step is wrong #ai #prompt #aiworkflow

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My site: https://natebjones.com
Full Story w/ Prompts: https://natesnewsletter.substack.com/p/the-prompt-doctor-is-in-fixes-for?r=1z4sm5&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true
My substack: https://natesnewsletter.substack.com/
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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.

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For deeper playbooks and analysis: https://natesnewsletter.substack.com/