When I first started working with AI tools, I was genuinely stunned. The power, the speed, the breadth of what it could do, it felt like someone had handed me a turbocharger for my brain. I dove in headfirst.
Then it burned me.
Not once. Not twice. AI confidently gave me wrong answers, fabricated references, and produced outputs that looked exactly right until I went to use them and discovered they weren’t. The term for this is hallucination, and if you haven’t experienced it yet, you will. It doesn’t announce itself. It arrives dressed as confidence.
That experience forced me to rethink my entire approach.
The Mindset Shift That Changed Everything
Here’s where I landed, and I’ll warn you it sounds counterintuitive at first:
I now operate from the assumption that AI is wrong.
Stay with me.
I don’t use AI as an oracle. I use it as a very fast, very capable first draft that I immediately put on trial. When AI gives me an answer, my default posture isn’t acceptance, it’s challenge. I push back. I ask it to justify its reasoning. I make it show its work. I probe the edges of its conclusions to see where they hold and where they crack.
What I’m really doing is forcing the model to reevaluate its own output under pressure. And that process, that deliberate friction, consistently produces better results than simply accepting the first answer.
Why This Works
AI models are generative, not authoritative. They produce outputs that are statistically likely given the input, not outputs that are provably correct. That’s a meaningful distinction. A model can be confidently wrong because confidence and correctness are separate dimensions in how these systems work.
When you challenge an AI answer, a few things happen:
It surfaces uncertainty. A well-prompted model, under challenge, will often walk back overconfident claims or add nuance it left out the first time. That nuance is frequently where the real answer lives.
It forces you to engage critically. The act of challenging an answer requires you to actually think about whether it makes sense. That keeps you in the loop rather than becoming a passive consumer of AI output, which is a dangerous place to be in a professional context.
It improves the output iteratively. Each challenge-and-response cycle refines the answer. You’re not accepting draft one; you’re running draft one through a review process. The final output is categorically better than what you’d have gotten by stopping at the first response.
What This Looks Like in Practice
In my work doing deep performance engineering on Azure App Service, root cause analysis, network trace diagnostics, memory dump analysis, the stakes on accuracy are high. A wrong answer doesn’t just waste time; it sends a customer in the wrong direction.
So when AI surfaces a hypothesis about a connection failure or a thread pool behavior, I don’t just write it up. I interrogate it. I ask: what evidence supports this? What would contradict it? Is this consistent with what the packet capture actually shows? If there’s another explanation, what is it?
More often than not, the first answer is directionally right but imprecise. The challenged answer is defensible. That difference matters enormously when you’re writing a root cause analysis that a customer will read and act on.
The Practical Takeaway
If you want to get dramatically more out of AI, adopt this posture:
Treat every AI output as a hypothesis, not a conclusion. Your job is to decide whether the hypothesis survives scrutiny, not to copy it into whatever you’re building.
Ask it to defend itself. “Are you confident in that?” “What assumptions is this based on?” “What’s the strongest argument against this?” These prompts are not annoying to the model; they’re productive pressure that produces more rigorous output.
Verify the things that matter. AI is excellent at acceleration. It is not a replacement for professional judgment. Use it to get to the starting line faster, then apply your expertise to close the last mile.
The engineers getting the most out of AI right now aren’t the ones who trust it most. They’re the ones who’ve built a productive skepticism, who understand that the value of AI is unlocked not by acceptance, but by engagement.
Assume it’s wrong. Make it prove otherwise. You’ll be surprised how often it rises to the challenge, and how much better the work becomes when you do.
Christopher Corder is a Senior Azure Technical Advisor at Microsoft specializing in Azure App Service performance engineering, diagnostics, and root cause analysis. He writes about AI, cloud engineering, and the practical realities of working at the intersection of both.
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