Refactor Anytime
Source: Dev.to
The Traditional Fear
In traditional development, refactoring is risky:
- “We’re in the middle of a feature—don’t touch the foundation.”
- “Let’s finish this first, then clean up.”
- “Refactoring now will destabilize everything.”
These fears made sense when refactoring was expensive and error‑prone.
With AI, refactoring cost drops dramatically. The question shifts from “Can we afford to refactor?” to “Does our context support it?”
Context‑Dependent Regeneration
AI doesn’t modify code; it regenerates code from the provided context.
This changes when refactoring is possible:
| Condition | Refactoring Possible? |
|---|---|
| Context is clear and complete | ✅ Yes |
| Context is fragmented or stale | ❌ No – fix context first |
| Mid‑feature, but context is maintained | ✅ Yes |
| After a long break with no logs | ❌ No – rebuild context first |
Refactoring availability depends on context state, not project phase.
If you’ve maintained logs, kept artifacts aligned, and preserved decision history—you can refactor at any moment, even mid‑feature.
What AI Refactoring Produces
When you ask AI to refactor without constraints, it optimizes for separation of responsibilities.
| Human Expectation | AI Output |
|---|---|
| Consolidate duplicate code | Separate by responsibility |
| Reduce file count | Increase file count |
| DRY (Don’t Repeat Yourself) | Clear boundaries between concerns |
AI refactoring often increases code volume.
That isn’t wrong—it’s a different optimization target. AI prioritizes clarity of responsibility over brevity.
The DRY Problem
When you explicitly request DRY refactoring, a new problem emerges: scope limits.
AI can only see what’s inside its context window. When the codebase exceeds that window:
- AI sees only a partial duplication.
- AI proposes consolidation within the visible scope.
- Consolidation may conflict with code outside the scope.
- Result: broken references, incomplete abstractions.
Scale‑Dependent Instructions
| Codebase Size | Approach |
|---|---|
| Small (fits in context) | “Refactor for DRY” works |
| Medium | Specify which files to consider |
| Large | Break into explicit scope boundaries, refactor incrementally |
For large codebases, you must manage the scope explicitly:
Refactor these 3 files for shared utilities:
- /src/handlers/userHandler.ts
- /src/handlers/orderHandler.ts
- /src/handlers/productHandler.ts
Do not modify files outside this set.The Real Purpose of Refactoring
Refactoring with AI isn’t about aesthetics or best practices.
It’s about keeping code AI‑controllable.
As code grows, it becomes harder for AI to:
- Hold the full picture in context
- Understand relationships between components
- Make changes without unintended effects
Refactoring restructures code into units AI can manage:
| Before Refactoring | After Refactoring |
|---|---|
| Large files with mixed concerns | Smaller files with clear purposes |
| Implicit dependencies | Explicit interfaces |
| Tangled responsibilities | Separated responsibilities |
| Requires full context to understand | Each unit understandable in isolation |
Refactor when AI struggles to control the code. The struggle is the signal.
When to Refactor
Traditional timing:
- After a feature is complete
- During dedicated “refactoring sprints”
- When technical debt becomes unbearable
AI‑era timing:
- When context can support it (logs maintained, artifacts aligned)
- When code exceeds AI’s comfortable control scope
- When you notice AI making mistakes due to structural complexity
- Anytime the above conditions are met
Don’t wait for a “refactoring phase.” If context is clean and the code is becoming unwieldy, refactor now.
The Refactoring Loop
1. Notice AI struggling (repeated errors, confusion, partial understanding)
2. Check context state (are logs current? are artifacts aligned?)
3. If context is ready → refactor
4. If context is stale → rebuild context first
5. After refactor → update logs with the new structureRefactoring becomes part of the continuous flow, not a separate activity.
Refactoring Reveals Context Problems
When you refactor with human intent, expect surprises.
The result won’t match your expectations perfectly—something will be off: a structure you didn’t anticipate, separations that feel odd, boundaries in unexpected places.
These surprises aren’t AI failures. They’re context problems surfacing.
The mismatch reveals:
- Premises you assumed but never stated
- Priority orderings that existed only in your head
- Goals that weren’t clearly communicated
The Opportunity
Don’t fix the code. Fix the context.
When refactoring produces unexpected results:
- Identify the gap – What did AI produce vs. what you expected?
- Trace to context – Which premise, priority, or goal was missing?
- Rebuild context:
- Restate premises explicitly (and their order)
- Clarify the ordering of priorities
- Define purpose and its relative importance
Then refactor again with the corrected context.
Context Reconstruction Checklist
| Element | Question |
|---|---|
| Premises | What assumptions are we building on? In what order? |
| Priorities | What matters most? What can be sacrificed? |
| Purpose | What is this code trying to achieve? |
| Constraints | What limits apply? |
Refactoring is context debugging. The unexpected output is the error message.
Responsibility Separation vs. DRY
Accept this trade‑off:
| Goal | Result | AI Compatibility |
|---|---|---|
| Responsibility separation | More files, clearer boundaries | High – AI handles well |
| DRY consolidation | Fewer files, shared abstractions | Medium – requires scope management |
If you want DRY, you must invest in scope management. If you let AI lead, you get separation. Neither is wrong—just know which you’re choosing.
Summary
| Traditional | AI‑Era |
|---|---|
| Refactor after a feature is complete or during dedicated sprints. | Refactor whenever the context is clean and AI shows signs of struggling with the code’s structure. |
| Decisions are driven by human bandwidth and risk aversion. | Decisions are driven by context quality and AI’s ability to maintain control. |
| DRY is often the primary goal. | Separation of responsibilities is often the primary goal; DRY requires explicit scope handling. |
| Refactoring is a separate, scheduled activity. | Refactoring is a continuous, context‑driven loop. |
Keep your context tidy, and AI will keep your code tidy.
# Refactor When Context Supports It
## Risks
- **Risk of destabilization** – occurs only if the context is stale.
## Principles
- **Optimize for DRY** – keep code reusable and maintainable.
- **Optimize for AI controllability** – ensure the AI can reliably manage the codebase.
## Activity Types
- **Scheduled activity** – planned refactoring sessions.
- **Continuous capability** – ongoing improvements as the code evolves.
> **Refactoring is no longer a phase.**
> It's a tool you use when code outgrows AI's control—which can happen anytime.
*This is part of the **"Beyond Prompt Engineering"** series, exploring how structural and cultural approaches outperform prompt optimization in AI‑assisted development.*