AI is fast at rewriting code,
but not always good at refactoring it.
Left on its own, AI often rearranges code
instead of improving it,
or adds abstractions that don’t express meaning.

Good refactoring comes from guidance,
not from asking the model to “make it better.”

This page shows how to ask AI for refactoring
in a way that produces clarity, not churn.

AI refactors best when it knows why the code needs to change.

Instead of:

“Refactor this.”

Try:

“Refactor this for clarity and predictable shape.
No new abstractions.
No changes to behavior.”

Or:

“Refactor this to separate concerns: 
parsing, validation, and execution.”

QWYou’re giving the model a direction,
not a blank canvas.

AI needs boundaries to avoid drifting.

Tell it explicitly:

• what must not change
• what must remain stable
• what must not be introduced

Examples:

“Do not change the public API.”

“Do not introduce new classes.”

“Do not add static methods.”

“Do not change the return shape.”

“Do not add dependencies.”

These constraints keep the refactor grounded.

AI responds strongly to shape.

Examples:

“Return a readonly DTO instead of an array.”

“Split this into two small methods: 
one for validation, one for execution.”

“Use early returns to simplify branching.”

“Flatten nested conditionals.”

“Replace the switch with a map of handlers.”

Shape is the architecture.
AI will follow it.

One of the most effective techniques:

“Before rewriting, outline the structure you intend to use.”

AI will produce:

• method boundaries
• responsibilities
• data shapes
• flow
• dependencies

Once the structure is right,
the implementation becomes predictable.

AI drifts when the request is too large.

Instead of:

“Refactor this entire class.”

Try:

“Refactor only the validation logic.”
“Refactor only the constructor.”
“Refactor only the error handling.”

Small steps
keep the architecture intact.

This is where the collaboration becomes powerful.

Ask:

“Explain the changes you made and why.”

AI will reveal:

• assumptions
• patterns it borrowed
• abstractions it introduced
• misunderstandings about PHP
• places where it drifted

Once we see the reasoning,
we can correct the direction.

AI learns from our feedback within the session.

Examples:

“Good — this version has clearer boundaries.”

“Good — this removes unnecessary abstractions.”

“Good — this keeps the return shape stable.”

Positive reinforcement
helps the model internalize our architectural preferences.

AI sometimes:

• adds factories
• introduces interfaces
• creates abstract classes
• adds configuration arrays
• splits code into too many files

When this happens, we guide it gently:

“Simplify this.”

“Remove unnecessary abstractions.”

“Use a single class.”

“Keep the flow straightforward.”

Refactoring is about clarity,
not ceremony.

Sometimes AI only renames variables or rearranges lines.

Ask:

“Improve the architecture, not just the formatting.”

“Clarify the responsibilities.”

“Separate parsing from validation.”

“Extract a small helper method.”

AI will rise to the level of the request.

To get better refactoring from AI:

• We start with the goal
• define what must stay the same
• describe the shape we want
• ask for structure before code
• use small, intentional steps
• ask for explanations
• reinforce good decisions
• pull back over‑engineering
• push past superficial changes

AI can rewrite code quickly.
We guide it toward clarity.

Together, we produce refactors that are calm,
intentional, and aligned with modern PHP.

This page describes common behaviors observed in AI models when generating PHP code.
These patterns are not guarantees, but they are consistent enough to be useful when reviewing or guiding AI output.

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