There’s a lot of hype right now around using AI in software development. And to be fair, the hype isn’t entirely misplaced. AI can write code. It can summarize, generate, and even refactor. But there’s a major problem with the way most people use it. The problem is guessing.

When AI Has to Guess

If your prompt is not sufficiently clear, the AI has no choice but to guess what you meant. And this guessing introduces subtle, and sometimes major, errors. It might guess wrong about your naming conventions, your error-handling preferences, your architectural style, or your end goals. Even if you think the prompt is clear, the AI may read it differently than you do.

This is where most of the breakdown in AI-assisted development happens. The AI’s assumptions diverge from your intent and it confidently generates code that doesn’t do what you wanted. And unlike a junior developer who might ask for clarification, the AI just barrels forward, filling in gaps however it sees fit.

There’s a better way.

Let the AI Ask, Not Guess

Instead of relying on the AI to fill in the blanks, flip the script. Ask it to analyze your prompt and ask you about any gaps or assumptions it would otherwise make. This changes the nature of the interaction. It becomes a back-and-forth, like pair programming with someone who’s not afraid to admit when something isn’t clear.

For example, tell the AI:

Analyze the prompt delimited by the triple backticks. If there is anything that is not clear and executable, ask questions about any gaps where you may have to guess. For each question, provide three to five choices along with pros and cons for each choice. Then, give a recommendation and explain your reasoning.

That’s the starting point. You give the prompt and the AI gives you a list of questions about things it’s not sure about.

Making Choices and Adding Clarity

Once you get the list of questions, you walk through them. Make your choice. But you’re not limited to the choices the AI provides. You can write your own answer, clarify things further, or reframe the question entirely. You’re still the one steering the conversation.

The AI’s recommendations can help guide you, but you’re not handing over the wheel. You’re treating the AI like a helpful assistant who brings up options and tradeoffs, but who still looks to you for the final decision.

Iterate Until It’s Clear

This process is not one-and-done. One round of clarification is rarely enough. After you respond to the first round of questions, the AI should reanalyze your updated prompt. If anything is still unclear, it should ask again. New questions will surface as others get resolved.

You keep going until the AI says, “There is nothing unclear or ambiguous in your prompt.” At that point, you’re ready to move forward. You now have a prompt that is clear, unambiguous, and executable, one that a wide range of AI models can interpret in the same way you intended.

Clarity Over Guessing

This iterative method avoids the most common source of error in AI development: misinterpretation. Instead of letting the AI jump to conclusions, you walk it through the reasoning. You help it see what you’re trying to build. You clarify, step by step, until there’s no room left for guessing.

And the AI itself becomes the tool to help you reach that clarity.

Try Multiple Models

One additional tip: don’t rely on just one AI. Different models behave differently. They’ve been trained on different datasets and reflect different patterns of thought. One model might ask excellent questions about architecture. Another might catch nuances in naming conventions. Some might miss key gaps that others catch.

Just like getting advice from different consultants, trying a few different models can reveal blind spots in your prompt and help you refine it further. Even if you prefer one model for the final implementation, it doesn’t hurt to bounce ideas off another before you get there.

Final Thoughts

If you want to get the most out of AI in software development, stop treating it like a mind reader. It’s not. And it doesn’t need to be. What it needs is a clear, detailed, and executable prompt. You can get there through an iterative process of questions, choices, clarification, and feedback.

Don’t let the AI guess. Teach it to ask.

Then answer.