Stop Using ChatGPT Just to Write Code
Debugging your logic first saves hours of refactoring later.
I was struggling with a bug last Tuesday
The code looked fine. Tests were passing. But the feature kept failing in production.
I pasted the error into ChatGPT. It gave me a fix. I applied it. Still broken.
Turns out, the code wasn't the problem. My entire approach was wrong.
I was asking AI to fix syntax when I should have been fixing my thinking.
It felt wasteful. I spent hours tweaking lines that shouldn't have existed in the first place.
That's when I decided to change how I use the tool.
We treat AI like a junior dev who types
Most of us use these tools as super-powered autocomplete.
We paste errors, we ask for functions, we demand snippets.
It feels productive because you see text appearing on the screen.
But it doesn't know your business logic. It doesn't know your users.
It just knows patterns from public repos.
So you end up with perfect code that solves the wrong problem.
You build something fast, then spend twice as long refactoring it.
I realized I was optimizing for typing speed, not solution quality.
Here's the idea I started using
I stopped asking it to write code immediately.
Instead, I describe the problem in plain English first.
I treat the chat like a senior dev pair programmer.
Not someone who types, but someone who thinks.
I tell it my plan before I open my IDE.
I ask it to find holes in my logic before I write a single line.
It feels slower at first. You aren't seeing code generate.
But you catch big mistakes early.
The actual coding part becomes much faster because the path is clear.
How to debug your thinking (not just syntax)
- Describe the goal, not the code. Tell it what you want to achieve in plain English. Let it suggest the approach before you commit to a stack.
- Ask for edge cases. "What breaks this?" is a better question than "how do I write this?". Force it to think about failure scenarios.
- Challenge your assumptions. Tell it your logic. Ask it to argue against you. It finds gaps in your reasoning fast.
- Use it for data structure. Before writing queries, ask if your schema supports the load. Bad data design is hard to fix later.
- Review the flow. Paste your step-by-step plan. Ask where a user might get stuck or where latency could spike.
- Keep it honest. Tell it "I might be wrong here". It lowers the guardrails and gives sharper critique instead of polite agreement.
This actually helped me last week
I was building a payment webhook handler.
My plan was simple: receive webhook, update database, send email.
I typed this flow into ChatGPT and asked "What's wrong with this?".
It immediately pointed out race conditions.
If two webhooks hit at once, my DB updates would conflict.
It suggested a queue system instead of direct updates.
I hadn't even written the code yet.
I saved two days of refactoring and potential data corruption.
The code wasn't the issue. The architecture was.
Key takeaways
- Code is cheap, logic is expensive. Fixing a thought costs nothing. Fixing deployed code costs hours.
- Use AI to challenge your plan. Don't just use it to fill in syntax gaps.
- If the logic is wrong, the code doesn't matter. Perfect syntax on a broken idea is still broken.
- Treat it like a reviewer. Ask for critique, not just generation.
- You still need to understand. Don't blindy follow its advice. Verify the logic makes sense to you.
Try it on your next task
Next time you have a complex ticket, don't open your IDE.
Open the chat.
Fix the thinking first.
Write the code second.
Have you tried prompting for logic yet, or do you stick to code generation?
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