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Most PMs using AI to write presentations are producing slop. They just don't know it yet. Executives do. The tell is micro hallucinations. The slightly wrong number. The phrasing that's close but not quite yours. The recommendation that sounds confident but has no grounding. Individually, each one is invisible. In aggregate, they read as "AI wrote this and nobody checked." The fix isn't better prompts. It's more iterations. The approach that consistently beats slop: inventory before prompting. Before you open Claude, you write down everything you actually know about the topic. Your opinions. Your data. Your constraints. You bring the content. Claude brings the structure. When you reverse that, you get the AI's best guess at what a PM might know, which is exactly what slop sounds like. Then you layer in complexity slowly. Don't dump your goal in the first message. Start with the context. Build the scenario. Reveal the actual ask later, once the model has enough grounding to not hallucinate its way to an answer. The 5am presentation in this episode is the proof of concept. A product leader, no prep, builds a full Q2 roadmap presentation before most people have had coffee. The method: slides first, script second. Never click "enhance." Stop the model from suggesting next steps. And run 200 iterations on the language before calling it done. The difference between someone who uses AI and someone who uses it well. The biology metaphor from this episode is the one that stuck with me: you can't microwave a plant. You can speed up the process, but you can't skip the stages. The PMs who get clean output have built the discipline around AI, not just the access to it. Executives smell slop. Build the discipline before you're in the room.

Aakash Gupta

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