AI wrote your code, tests passed, you shipped it, and it broke in production.
Here is why that keeps happening and how to actually fix it.
Why It Happens?
AI models are trained on public code, which means they are also trained on bad code, outdated patterns, and deprecated APIs.
When you prompt Copilot or Cursor without context, it doesn't know your architecture, your environment variables, your edge cases. It's guessing confidently.
That's the real problem. It's not that the AI is careless. It's that it sounds certain even when it's working with none of the information that actually matters.
Here Is What You Can Do
Here is what senior engineers do differently.
They don't ask AI to write the solution. They ask it to challenge the solution.
Prompt it like this:
"Here is my implementation. What assumption am I making that could fail in production? What edge cases am I missing?"
Now you are using it as a code reviewer, not a code generator.
Always Include Your Actual Context
Paste your schema, your existing functions, your constraints.
A model with context beats a model without it every single time. Skip this step and you're not getting a solution, you're getting a guess dressed up as one.
Go Further
Extra helpful tip if you want to go further: set up a free commit hook that runs an AI review before anything reaches your repo.
Catch the confident guesses before they ship, not after.