For a long time correcting the AI was just part of the process.
Generate. Review. Fix the structure. Fix the naming. Move the logic to the right place. Ask again. Get something closer. Fix that too.
I thought that was normal. I thought that was just how working with AI worked. You prompt, you steer, you correct, you move on.
It took me a while to realize that the corrections were not a feature of the workflow. They were a symptom of a broken one.
What the corrections were actually telling me
Every time I corrected the AI I was communicating a rule.
Not in a way the AI could retain. Not in a way that would apply to the next session or the next developer or the next feature. Just a one-time correction that fixed that specific output in that specific moment.
The component was too large. I split it. The state was in the wrong place. I moved it. The naming did not follow the convention. I renamed it.
Each correction took a few minutes. Across a session it added up to an hour. Across a week it felt like half my time was spent steering instead of building.
And none of it carried forward. The next session started exactly where the last one did. Same corrections. Same patterns. Same hour of steering.
The shift that changed everything
I started writing the corrections down.
Not as feedback. Not as comments. As rules. The things I kept correcting became constraints I gave the AI before the session started.
Components have one responsibility. If it does more, split it before continuing. State lives in hooks. Never inline in the UI. Names reflect the domain. No generic placeholders.
The first session with those rules felt different immediately. The output came out closer to what I expected. Not perfect. But the corrections I had been making every session just did not appear.
I did not have to move the state. It was already in the right place. I did not have to rename things. The naming already followed the convention. I did not have to split the component. It was already the right size.
What stopped happening
The hour of corrections did not gradually shrink. It mostly disappeared.
Not because the AI got smarter. Because the rules defined what the output had to look like before the AI generated anything. There was nothing left to correct that the rules had already handled.
The sessions changed too. Instead of spending time steering the AI toward the standard, I spent time on the actual problem. The feature. The logic. The product decision. The things that actually required my judgment.
The AI handled the standard. I handled everything else.
Here is what the rules looked like that made that possible:
Rules that replaced my most common corrections:
1. Every component is presentational or container. Never both. If it starts doing both, stop and extract.
2. All state logic lives in a dedicated hook within the feature. No exceptions for small state.
3. Names describe what something does in domain terms. No data, items, flag, or handler without context.
Three rules. Written once. Applied to every session. The corrections those rules covered never came back.
What I understood that day
Rules are not constraints on the AI. They are corrections you made once and never have to make again.
Every rule in my system started as a correction. Something the AI did that I fixed. Something I fixed again the next session. Something I eventually got tired of fixing and wrote down instead.
The system grew from the corrections. And as it grew the corrections shrank. Because every rule I added was one less thing I had to fix by hand.
That is what rules actually do. They move the correction upstream. From after the output to before it. From reactive to preventive. From your time to the system's job.
The prompt does not matter. The rules do.
If you are spending time correcting your AI after every session, you are not experiencing a limitation of AI.
You are experiencing rules that have not been written down yet.
Write them down. Give them to the AI before the session starts. And watch the corrections disappear one by one.
Want to find which corrections you keep making that should already be rules?
I built a free 24 point checklist that helps you find exactly that. The structural gaps where your AI keeps making the same decisions you keep fixing.
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