How to Use AI for Code Refactoring: A Practical Guide

AI coding tools have gotten remarkably good at generating new code, but their most underrated capability in 2026 is code refactoring. Restructuring a tangled module, extracting a service class, or migrating an API — these are tasks where AI assistance delivers the biggest productivity gains because refactoring is tedious, error-prone, and requires touching many files simultaneously.

This guide walks you through practical techniques for using AI to refactor real codebases. Not toy examples — actual patterns you’ll encounter in production TypeScript, Python, and other languages. I’ll cover which AI tools work best for different refactoring scenarios, common pitfalls to avoid, and a step-by-step workflow you can adopt immediately.

Why AI Excels at Refactoring (and Where It Doesn’t)

Refactoring is a sweet spot for AI coding tools for several reasons:

• Pattern recognition — AI is excellent at recognizing code smells and suggesting established design patterns
• Mechanical transformations — renaming, extracting, and moving code across files is tedious for humans but trivial for AI
• Consistency — AI can apply the same refactoring pattern uniformly across hundreds of call sites
• Test awareness — good AI tools understand that refactoring requires updating tests alongside production code

However, AI has clear limitations in refactoring:

• Architectural judgment — AI can execute a refactoring, but deciding what to refactor and how still requires human judgment
• Business context — AI doesn’t know that “the payment module is fragile because we’re migrating providers next quarter”
• Risk assessment — it can’t gauge the organizational impact of a large refactoring

The best approach is to use AI as an execution engine while you provide the strategic direction. You decide what needs refactoring and why; the AI handles the mechanical work of making it happen across your codebase.

Choosing the Right AI Tool for Refactoring

Not all AI coding tools are equally good at refactoring. The key requirement is multi-file awareness — refactoring almost always touches multiple files, and tools that only understand the current file will produce broken code.

Best Tools for Refactoring in 2026

For the examples in this guide, I’ll primarily use Cursor Composer and Claude Code, as they consistently produce the best results for refactoring tasks. The techniques apply to any agentic AI tool, though.

Refactoring Pattern 1: Extract and Reorganize Modules

The most common refactoring task is breaking apart a file that’s grown too large. Here’s how to do it effectively with AI.

The Problem

You have a 1,200-line userService.ts that handles authentication, profile management, notification preferences, and admin operations. It needs to be split into focused modules.

The AI-Assisted Approach

Step 1: Analyze the current structure. Start by asking the AI to map the file:

// In Cursor Composer or Claude Code:
"Analyze userService.ts and identify logical groupings of functions. 
List each group with its dependencies and the functions that belong to it.
Don't make changes yet — just analyze."

This analysis step is critical. The AI will identify natural boundaries you might miss, and it prevents the AI from making changes before you’ve approved the plan.

Step 2: Approve and execute the split. Once you agree with the groupings:

"Split userService.ts into these modules:
- auth.service.ts (login, logout, refreshToken, validateSession)
- profile.service.ts (getProfile, updateProfile, uploadAvatar, deleteAccount)
- notifications.service.ts (getPreferences, updatePreferences, unsubscribe)
- admin.service.ts (listUsers, banUser, resetPassword, auditLog)

Create a barrel export in services/index.ts. Update all imports across the project."

A good AI tool will:

1. Create the four new files with the correct functions
2. Move shared types and utilities to a separate types.ts or keep them in the appropriate module
3. Create the barrel export
4. Update every import statement across your project (this is the part that saves the most time)
5. Update test files to import from the new locations

Common Pitfall: Circular Dependencies

When AI splits a module, it sometimes creates circular dependencies. For example, auth.service.ts might need something from profile.service.ts and vice versa. Watch for this in the diff review, and if it happens, ask the AI to extract the shared dependency into a separate utility file.

Refactoring Pattern 2: Type Safety Improvements

Adding or improving type safety is a perfect AI refactoring task because it’s mechanical but touches many files.

Example: Replacing any Types in TypeScript

// Prompt for Cursor Composer:
"Find all uses of 'any' type in the src/ directory. For each one:
1. Infer the correct type from usage context
2. Create any new interfaces/types needed in the appropriate types file
3. Replace 'any' with the correct type
4. Make sure all call sites are type-safe

Start with the API layer (src/api/) and work outward.
Skip test files for now."

This kind of systematic refactoring is where AI truly shines. A human developer would spend hours tracing through call sites to determine the right type. The AI can analyze usage patterns across files and infer types in seconds.

Example: Adding Zod Validation to API Endpoints

// Prompt:
"Add Zod request validation to all Express route handlers in src/api/routes/.
For each endpoint:
1. Create a Zod schema that matches the expected request body/params/query
2. Add validation middleware before the handler
3. Use z.infer to derive TypeScript types from the schema
4. Replace any manual validation in the handler with the Zod schema

Follow the pattern already established in authRoutes.ts."

The key instruction here is “follow the pattern already established in authRoutes.ts.” By pointing the AI to an existing example, you ensure consistency across the codebase.

Refactoring Pattern 3: Design Pattern Migration

Migrating from one design pattern to another — say, callbacks to async/await, class components to hooks, or Express to Fastify — is a high-value refactoring where AI saves enormous time.

Example: Class Components to React Hooks

// Prompt:
"Convert all React class components in src/components/ to functional 
components with hooks. For each component:
1. Convert lifecycle methods to useEffect hooks
2. Convert this.state to useState
3. Convert this.props to function parameters with destructuring
4. Preserve all existing behavior and prop types
5. Update the corresponding test file

Process one component at a time so I can review each conversion."

The instruction to “process one component at a time” is important for large refactoring tasks. It lets you catch errors early rather than reviewing 20 converted components at once. Most agentic AI tools handle this well — they’ll convert one component, show you the diff, and wait for approval before continuing.

Example: REST to GraphQL Migration

// Prompt:
"I'm migrating the user-facing API from REST to GraphQL. 
The REST endpoints are in src/api/routes/. For each endpoint:
1. Create a corresponding GraphQL resolver in src/graphql/resolvers/
2. Define the GraphQL types in src/graphql/schema/
3. Keep the existing REST endpoints working (we'll deprecate later)
4. Reuse the existing service layer — resolvers should call the same 
   service functions as the REST handlers

Start with the User entity: GET /users, GET /users/:id, POST /users, 
PUT /users/:id, DELETE /users/:id"

This kind of migration is where agentic AI tools save days of work. The AI understands both REST and GraphQL patterns and can create the schema, resolvers, and types while reusing your existing business logic.

Refactoring Pattern 4: Database Query Optimization

AI can identify and fix common database performance issues, though you should always verify the results with your actual query plans.

Example: N+1 Query Elimination

// Prompt:
"Audit all database queries in src/services/ for N+1 query problems.
For each N+1 pattern found:
1. Show me the current code and explain the N+1 issue
2. Rewrite using eager loading (Prisma include) or batch queries
3. Add a comment explaining the optimization

Focus on the Order and Product services first — those are the 
highest-traffic endpoints."

In my experience, AI tools correctly identify N+1 patterns about 90% of the time and provide correct fixes about 80% of the time. The remaining 20% usually need minor adjustments — the AI might over-eagerly include relations that aren’t needed, which can actually hurt performance.

Example: Raw SQL to ORM Migration

// Prompt:
"Convert all raw SQL queries in src/repositories/ to Prisma queries.
For each conversion:
1. Maintain identical behavior (same filters, ordering, pagination)
2. Use Prisma's type-safe query builder
3. Add proper TypeScript return types
4. Keep the raw SQL as a comment above the Prisma query for reference
   during the transition period"

Refactoring Pattern 5: Test Coverage Improvements

One of the best uses of AI in refactoring is generating tests for untested code before you refactor it. This gives you a safety net.

The “Test-First Refactoring” Workflow

1. Generate tests for existing behavior:

   // Prompt:
   "Write comprehensive unit tests for OrderService.ts. 
   Cover all public methods including edge cases and error paths.
   Use Jest and follow the testing patterns in src/__tests__/UserService.test.ts.
   Mock external dependencies (database, email service, payment gateway).
   Aim for >90% branch coverage."

2. Run the tests to verify they pass:

   npx jest src/__tests__/OrderService.test.ts --coverage

3. Now refactor with confidence: With tests in place, you can ask the AI to refactor the module knowing that test failures will catch regressions.
4. Update tests if the API changes:

   // Prompt:
   "I've split OrderService into OrderService and PaymentService.
   Update the tests to match the new structure. Keep the same test 
   cases but point them at the correct service."

This workflow dramatically reduces the risk of refactoring. In my experience, AI-generated tests catch about 60-70% of the regressions that would otherwise slip through. Combined with your existing tests, you get solid coverage.

The Refactoring Workflow: A Step-by-Step Process

Here’s the workflow I’ve refined over six months of AI-assisted refactoring:

Step 1: Create a Snapshot

Before any refactoring, create a git branch and ensure your test suite passes:

git checkout -b refactor/extract-user-modules
npm test  # Ensure everything passes before refactoring

Step 2: Analyze First, Change Later

Always start with an analysis prompt. Don’t let the AI start making changes until you’ve reviewed its plan:

"Analyze the current state of src/services/userService.ts.
Identify code smells, suggest refactoring opportunities, and 
propose a plan. Don't make any changes yet."

Step 3: Execute in Small Batches

Break large refactoring into manageable chunks. Review each chunk before proceeding:

"Execute step 1 of the refactoring plan: extract the authentication 
functions into auth.service.ts and update all imports."

Step 4: Verify After Each Chunk

Run your tests and type checker after each change:

npm test && npx tsc --noEmit

If tests fail, share the failures with the AI and let it fix them before continuing.

Step 5: Commit Incrementally

Commit after each successful chunk. This gives you rollback points:

git add -A && git commit -m "refactor: extract auth service from userService"

Step 6: Final Review

After all chunks are complete, review the full diff against the original branch:

git diff main...HEAD --stat

This shows you the total scope of changes and helps catch anything that slipped through individual reviews.

Advanced Techniques

Using AI for Architecture Decision Records

Before major refactoring, ask the AI to generate an Architecture Decision Record (ADR):

"Create an ADR for the proposed refactoring of the payment module.
Include: context, decision, alternatives considered, consequences, 
and migration plan. Format as markdown."

This creates documentation that explains why you refactored, which is invaluable when future developers (or future you) encounter the new structure.

Refactoring Across Language Boundaries

AI tools can refactor across language boundaries — for example, updating both a Python backend API and a TypeScript frontend client when an API contract changes:

"I'm renaming the 'user_profile' API endpoint to 'account'. 
Update all references in:
1. Python backend (src/api/routes.py, src/api/schemas.py)
2. TypeScript frontend (src/api/client.ts, src/hooks/useAccount.ts)
3. API documentation (docs/api.md)
4. Integration tests (tests/integration/)"

Claude Code and Cursor Composer both handle cross-language refactoring well, as long as both languages’ files are in the same project.

Measuring Refactoring Impact

After refactoring, ask the AI to quantify the improvement:

"Compare the before and after states of this refactoring:
1. Lines of code per file (was anything actually simplified?)
2. Cyclomatic complexity of the affected functions
3. Import depth (are dependencies simpler?)
4. Test coverage change"

This gives you concrete metrics to justify the refactoring effort to stakeholders.

Common Mistakes to Avoid

1. Refactoring Without Tests

The biggest mistake is refactoring code that has no test coverage. If you don’t have tests, generate them first (see Pattern 5 above). AI makes this fast enough that there’s no excuse to skip it.

2. Too-Large Refactoring Prompts

Asking the AI to “refactor the entire backend” will produce poor results. Break it into focused tasks: one module, one pattern, one file at a time. The AI’s quality degrades significantly when asked to handle too much at once.

3. Not Reviewing Diffs

AI-generated refactoring diffs should be reviewed with the same rigor as human code reviews. Watch for:

• Behavior changes disguised as refactoring (the AI might “improve” logic you didn’t ask it to change)
• Missing edge cases that the original code handled
• Over-abstraction (adding unnecessary layers of indirection)
• Inconsistent naming with the rest of your codebase

4. Ignoring Performance Implications

Some refactoring patterns improve code clarity at the cost of performance. For example, extracting a function that’s called in a hot loop can add overhead. The AI doesn’t always consider performance implications, so you need to watch for this in performance-critical code.

5. Not Updating Documentation

After refactoring, your README, API docs, and architecture docs may be out of date. Add this to your refactoring prompt:

"After making the code changes, update any documentation files 
(README.md, docs/) that reference the old module structure."

Refactoring Checklist

Use this checklist for every AI-assisted refactoring session:

• [ ] Created a feature branch before starting
• [ ] Tests pass before refactoring begins
• [ ] Generated tests for untested code first
• [ ] Asked AI to analyze before making changes
• [ ] Reviewed the AI’s plan before approving execution
• [ ] Executed refactoring in small, reviewable chunks
• [ ] Ran tests after each chunk
• [ ] Committed incrementally for rollback capability
• [ ] Reviewed the full diff for behavior changes
• [ ] Updated documentation and comments
• [ ] Ran the type checker (TypeScript) or linter
• [ ] Checked for circular dependencies
• [ ] Verified no debug code was left behind
• [ ] Performed a final manual smoke test

The Bottom Line

AI-assisted refactoring is one of the highest-value applications of AI coding tools in 2026. The mechanical work of moving code between files, updating imports, maintaining type safety, and ensuring test coverage — this is exactly the kind of tedious, error-prone work that AI handles well.

The key is to use AI as an execution engine, not a decision-maker. You provide the architectural vision and review the results; the AI handles the implementation. This human-AI collaboration produces better results than either could achieve alone.

Start with small refactoring tasks to build trust in the AI’s output — extract a single function, rename a module, add types to one file. As you develop a feel for what the AI handles well and where it needs guidance, scale up to larger refactoring projects.

The tools are ready. Your codebase will thank you.

For more on the specific AI tools mentioned in this guide, check out our Best AI Coding Tools for 2026 roundup, our Claude Code vs Cursor comparison, or our Cursor vs Copilot comparison for detailed reviews of the recommended tools. Cursor Claude Code / Anthropic