Why “Repair The whole lot” Is a Horrible Immediate

I discovered this lesson the onerous method. A number of occasions, really. And I’ve acquired the git historical past to show it.

There I used to be, observing a React element that had ballooned to over 500 strains. You realize the sort – began easy, then grew tentacles as options acquired tacked on. The traditional “I’ll clear this up later” that by no means occurred. We’ve all been there, proper?

With Claude in my nook, I figured I’d simply say “Hey, refactor this whole element,” and watch the magic occur. I imply, why not? That’s what AI is for, proper?

Mistaken. So very flawed.

The Nice Refactoring Catastrophe

“Refactor this element to be extra modular and maintainable,” I informed it confidently, leaning again in my chair with a smug grin, already imagining how impressed my crew can be.

Claude spat out a beautiful-looking refactor. Customized hooks extracted. Logic separated. Props destructured neatly. It even added JSDoc feedback. On the floor, it seemed like precisely what I wished.

I copied it in, ran the app, and… nothing loaded. Not simply the element – three completely different pages broke. That smug grin? Gone. Changed with pure panic.

Seems, the AI had silently damaged state dealing with throughout a number of parts. It renamed some key variables however missed references elsewhere. It eliminated what seemed like duplicate code that really dealt with refined edge circumstances.

The worst half? Fixing it took me longer than if I’d simply refactored manually. I may have used git to revert the change, however some components had been really working effectively that I wished to maintain. The changeset was so massive I couldn’t simply maintain one piece and discard the remaining.

Since all the things was tangled collectively in a single huge replace, I needed to maintain all of it and repair it manually. I spent hours tracing by the adjustments, attempting to determine what went flawed, earlier than lastly accepting defeat and beginning over.

Why AI Fails at Massive Refactors

After a couple of extra painful experiments (as a result of apparently I’m a glutton for punishment), I spotted why asking AI to “repair all the things” fails:

• It loses context throughout giant information. AI struggles to maintain observe of all of the interconnections in complicated code. It’s like asking somebody to reorganize your storage with out telling them which cables hook up with which gadgets.
• It doesn’t actually perceive your structure. It will possibly’t see how that one operate impacts three different parts. It’s working in isolation whereas your app is an ecosystem.
• It makes silent trade-offs. It would prioritize cleaner construction over sustaining each characteristic, dropping issues it thinks are unimportant. However who gave it permission to resolve what’s vital?
• It restructures based mostly on patterns, not enterprise logic. It should fortunately create elegant abstractions that fully miss the purpose of what your code must do. Fairly code that doesn’t work continues to be damaged code.

The outcomes are predictable:

• Damaged references (like if you rename a variable however neglect to replace it all over the place)
• Lacking unwanted effects (goodbye, analytics monitoring!)
• Over-engineered easy code (three hooks for what was once 5 strains)
• Silently dropped performance (it’s not a bug, it’s a characteristic… that’s now lacking)

A Higher Approach: Create an AI Refactoring Framework

After these painful classes, I’ve since discovered a couple of issues about taking small bites off of this elephant that often assist. And by often, I imply once I really comply with my very own recommendation as a substitute of getting impatient.

Step 1: Set up a Git Baseline

This one’s apparent however I nonetheless neglect half the time. Make a clear department. Commit. That is your panic button for when issues go sideways.

Section 1: Evaluation First

As a substitute of “repair all the things,” I begin with:

Evaluation this code and create a Markdown file of what must be improved or modularized. Do not rewrite something but.

This forces AI to research with out altering, giving me a roadmap as a substitute of a substitute. Creating one thing concrete like a file permits you to use an artifact to collaborate with AI on.

It’s like asking somebody to have a look at your messy storage and make a listing of what wants organizing earlier than they begin transferring issues round. Approach much less harmful.

Section 2: Human Suggestions

As soon as the checklist is in place, I’ll evaluation that checklist myself and:

• Cross off something pointless (“No, I don’t have to convert each operate to an arrow operate, thanks”)
• Add my very own priorities (“This state administration is the true downside”)
• Mark dangerous adjustments (“Don’t contact the fee processing logic!”)

This retains me in command of the method. The AI continues to be only a device, not the decision-maker. Even when I don’t know what the hell I’m doing (half the time), I’ll make an observation like “repair this to the most effective of your information” to depend on the mannequin’s experience as a substitute of mine (or lack thereof).

Section 3: Break It Down

Subsequent, I cut up the guidelines into small, manageable chunks. For instance:

1. Extract kind validation logic to a customized hook
2. Create separate parts for every kind part
3. Refactor state administration

The important thing: I solely deal with ONE merchandise at a time. One. Singular. Uno. That is the distinction between success and catastrophe.

It’s like consuming an elephant – one chunk at a time. Attempt to swallow it complete and also you’ll choke. (Not that I advocate consuming elephants. Please don’t.)

Section 4: Execute in Isolation

For every process:

• Work in a department (see Step 1, which I nonetheless neglect typically)
• Give AI very particular directions like “Extract solely this block right into a hook known as useValidation. Don’t change the rest.”
• Commit after every profitable change

Mainly, deal with AI like that new junior dev who’s actually keen however typically misunderstands necessities. You wouldn’t inform them “refactor the entire app” on day one, would you?

Section 5: Confirm and Restore

After every change:

• Run checks (you’ve checks, proper? …proper?)
• Examine performance manually (click on all of the buttons, fill all of the varieties)
• If one thing breaks: roll again and retry with a extra restricted scope
• If it really works: verify it off the checklist and proceed

This cycle retains you from getting too far down a damaged path. It’s like driving with common checkpoints as a substitute of ready till you’re fully misplaced to verify the map.

Ideas That Save Me Each Time

• Use breadcrumb feedback: Add feedback like // Handles kind validation to assist AI perceive what code does. It’s like labeling the packing containers in your storage earlier than asking somebody to reorganize it.
• Hold it scoped: Say “Solely extract this operate, don’t change variable names” as a substitute of “Clear this up.” Be bossy and particular. AI doesn’t have emotions to harm.
• Count on failure: Use Cursor checkpoints, Git, or simply maintain the unique code in a separate file. Assume issues will break and plan for it.
• Construct incrementally: Profitable refactoring occurs step-by-step, not all of sudden. That is true with or with out AI, however AI makes it much more crucial.

The Actual Magic of AI Refactoring

As soon as I began breaking issues down this manner, I discovered the candy spot. AI went from being my demolition crew to my junior developer.

It’s wonderful at:

• Extracting a single operate to a hook (it might probably deal with this transformation fantastically)
• Changing class parts to practical ones (when guided)
• Implementing a sample you describe intimately
• Including sort definitions or documentation (critically, it’s wonderful at this)

It’s horrible at:

• Understanding your whole codebase (it doesn’t have the context)
• Making architectural choices (it doesn’t know your enterprise wants)
• Realizing which edge circumstances matter to your customers
• Preserving enterprise logic it doesn’t perceive (that bizarre exception for California customers? Gone.)

Ultimate Ideas

Refactoring with AI isn’t about asking it to “repair all the things.” It’s about utilizing AI as a power multiplier on your personal experience.

Break down the work. Give particular directions. Hold a rollback plan.

The AI isn’t the architect – you might be. The AI is only a actually good keyboard that may write code quicker than you may sort. When you embrace that actuality, refactoring with AI turns into not simply potential, however extremely highly effective.

Simply keep in mind: one change at a time. And possibly, simply possibly, create that git department first.