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

Most developers write one prompt and accept whatever AI gives them. But the pros treat prompting like debugging - refining and improving with each exchange.

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Have you ever felt frustrated when AI didn't generate quite the code 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 refactoring code - you start with working functionality and gradually improve until you get exactly what you need.

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When you get an AI code response 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 code 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 code response that was almost perfect but missed something important?

Start broad to gauge the AI's understanding, then get specific. If you ask for "a function to process data" and get generic code, your updated prompt might specify data types, error handling, and performance requirements.

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 code output, ask: Does this follow best practices? Are edge cases handled? What's missing or inefficient?

Then use that analysis to refine. "Add input validation," "Include error handling," or "Optimize for performance."

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