You know that feeling when you ask someone to do something, they nod confidently, disappear for three hours, and return with exactly the opposite of what you wanted? Well, I used to have that same relationship with AI models when asking them to write code for me.
Then I discovered a simple phrase that changed everything: "Let me know if this makes sense to you and ask me any clarifying questions you have before proceeding."
I know, I know. Revolutionary stuff, right? But hear me out.
The Problem We Don't Talk About
Most of us treat AI like that overeager intern who says "absolutely!" to everything. We fire off our requirements, hit enter, and pray to the tech gods that what comes back is actually usable. More often than not, we get code that technically works but misses the mark entirely.
Sound familiar? You ask for a login system and get something that would make a security expert weep. You request a "simple" data visualization and receive what looks like a NASA mission control dashboard.
The Magic Happens in the Pause
Here's what I learned: The best collaborations (whether with humans or AI) happen when you create space for questions before the work begins, not after.
When I started ending my coding requests with that clarifying phrase, something amazing happened. Instead of diving headfirst into code, the AI would actually think through my request and come back with questions like:
"Should this handle edge cases like empty datasets?" "Do you need this to be mobile-responsive?" "What's your preference for error handling?" "Are there any specific libraries or frameworks you're already using?"
Suddenly, I wasn't just getting code. I was getting better code. Code that anticipated problems I hadn't even considered.
Why This Works So Well
1. It Forces Clarity Up Front
That pause for questions reveals assumptions on both sides. Maybe you assumed they'd know you're working in Python 3.9, or they assumed you wanted a REST API when you actually needed GraphQL.
2. It Catches the "Gotchas" Early
AI models are surprisingly good at spotting potential issues, if you give them permission to speak up. They might catch security vulnerabilities, performance bottlenecks, or compatibility issues before they become expensive problems.
3. It Creates True Collaboration
Instead of a one-way transaction, you get an actual back-and-forth. The AI becomes more like a thoughtful colleague and less like a code-generating vending machine.
4. It Saves Time (Really)
Yes, it adds a step upfront. But it's far faster than the alternative cycle of: generate code, realize it's wrong, clarify requirements, regenerate, repeat until coffee runs out.
The Unexpected Side Benefits
This approach has made me a better communicator overall. When you know someone is going to ask clarifying questions, you naturally become more specific in your initial requests. I now catch my own vague requirements before hitting send.
It's also made me more aware of my own assumptions. When the AI asks, "Should this handle multiple user roles?" I might realize I hadn't even considered user permissions yet.
Making It Your Own
You don't have to use my exact phrase. The key is signaling that questions are welcome and expected. Try variations like:
"Please clarify anything that seems ambiguous before you start coding." "What additional details would help you write better code for this?" "Are there any important considerations I might have missed?"
The goal is creating that collaborative pause where good planning happens.
The Bottom Line
AI is incredibly powerful, but it's not psychic. That simple phrase transformed my AI interactions from a frustrating guessing game into genuine collaboration. My code got better, my requirements got clearer, and my coffee stays warm longer.
In a world where we're all learning to work alongside AI, maybe the most important skill isn't prompt engineering, but remembering to ask if we're even building the right thing in the first place.
What techniques have you found for getting better results from AI? Send me an email and tell me about your experiences!