Many people use Claude Code to write code, but they are actually still stuck in the mindset of "fix wherever it breaks."

This is also one of the easiest ways to mess up a project. Recently, I’ve been continuously modifying and improving my project by learning from the ideas of industry experts.
Because bugs in real projects are usually not isolated small holes.
When you see error messages, page anomalies, or a button failing, it’s likely just a sharp corner of a system problem popping up.
If you directly give this sharp corner to AI and say "Help me fix it," it’s very likely only going to smooth out the sharp corner.
On the surface, it looks fixed, but the underlying logic hasn’t been changed.
Next time, in a different scenario, the same problem will blow up again.
And then, with more patches, the code is increasingly functional, but you become more afraid to make changes.
So now, when I use Claude Code, I have a very important habit:
Don’t just ask it to modify the code immediately; first, let it analyze why the problem occurs.
I prefer to ask like this:
Don’t fix it yet.
Help me analyze the root cause behind this issue.
Could it be a problem with one layer of design, state flow, data structure, or boundary conditions?
At this point, the role of AI changes.
It’s not just an "outsourcing bug fixer," but a "system troubleshooting assistant."
This distinction is huge.
And you shouldn’t just give it a single error message; instead, try to provide the full context: relevant files, call chains, user operation paths, expected results, actual results, and what has been changed before.
The more complete the context, the more likely it is to help you see the real problem, rather than just patching the surface.