You've mastered GenAI tools for project management tasks. Now let's unlock a game-changing skill: iterative prompting.

Most project managers write one prompt and accept whatever AI gives them. But the pros treat prompting like project refinement - iterating and improving with each exchange.

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Have you ever felt frustrated when AI didn't generate quite the project deliverable you wanted?

Iterative prompting is the systematic process of refining your prompts based on AI's previous responses to get better results.

Think of it like project planning - you start with a working framework and gradually improve until you get exactly what you need.

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When you get an AI response for a project task that's "close but not quite right," what do you usually do?

Here's the basic cycle: Start with your initial prompt, analyze the AI's output critically, then refine your prompt based on what worked and what didn't.

Each round gets you closer to your ideal solution. And you edit your original prompt every time to get to the perfect state.

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Can you think of a recent AI response for a project task that was almost perfect but missed something important?

Start broad to gauge the AI's understanding, then get specific. If you ask for "a project timeline" and get generic output, your updated prompt might specify stakeholders, dependencies, and risk factors.

Don't try to cram every requirement into your first prompt. Let the AI show you what it understands, then guide it.

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Have you ever tried editing your original prompt instead of building the thread?

When analyzing AI output for project tasks, ask: Does this follow PM best practices? Are stakeholders considered? What's missing or unrealistic?

Then use that analysis to refine. "Add risk mitigation," "Include stakeholder impact," or "Consider resource constraints."