Like most developers today, I've been exploring AI tools to improve my workflow. But I wanted to move beyond simple experiments—I needed to see if AI could handle a real, messy refactoring task. So I put Claude to the test with the PostFlow codebase.
The Challenge
PostFlow is a Django application that works well but carries a year's worth of technical debt. The symptoms were familiar to anyone who has shipped fast:
- Monolithic views handling too many responsibilities
- Business logic tangled with presentation code
- Duplicated code across different modules
- Inconsistent naming conventions
I faced a choice: spend weeks refactoring manually, or use Claude as a coding partner while I focused on architecture decisions. I chose the latter.
The Approach
I used Claude Code in the terminal, which provides direct code navigation and editing capabilities. But the key was how I structured the work.
Setting Clear Boundaries
I gave Claude specific refactoring targets:
- Extract business logic from views into dedicated service classes
- Create reusable components for common functionality
- Standardize naming conventions and improve code organization
- Maintain backward compatibility with existing functionality
Creating an Architect Agent
I created a custom Claude agent with specific prompts to guide the refactoring process. The agent's role was architectural—understanding the system design and making structural improvements, not just cosmetic changes.
The Workflow
- Define the scope: I specified exactly which files and patterns to refactor
- Request a plan: I asked Claude to outline the refactoring strategy before writing code
- Review changes: I tested every modification to ensure correctness
- Iterate on feedback: When changes didn't match my vision, I provided specific feedback for revisions
This iterative approach proved crucial. Claude is powerful, but it's not omniscient—it needs clear direction and correction.
What Worked Well
Speed: The refactoring took about 30% of the time manual refactoring would have required. Claude handled the tedious work of extracting methods, reorganizing imports, and updating references.
Consistency: Once I established patterns, Claude applied them consistently across the codebase. No more forgetting to update one file out of twenty.
Focus: I spent my time on architecture decisions—where to draw boundaries between components, how to structure services—while Claude handled the mechanical work.
What Required Attention
Specificity matters: Vague instructions produced inconsistent results. "Refactor the views" was too broad. "Extract the authentication logic from UserProfileView into an AuthService class" worked much better.
Review everything: Claude occasionally misinterpreted requirements or made assumptions that didn't align with my mental model. Every change needed review.
Context limitations: Claude sometimes lost track of broader architectural decisions across multiple files. I had to periodically remind it of the overall structure.
Test coverage is essential: Having tests made it easy to verify that refactoring preserved functionality. Without tests, this approach would be risky.
The Results
The refactored codebase is noticeably better:
- Views are focused on request handling and response formatting
- Business logic lives in service classes with clear responsibilities
- Common functionality is centralized in reusable components
- The code is easier to understand and modify
More importantly, the refactoring uncovered several bugs that were hiding in the tangled original code. The cleaner structure made them visible.
Lessons Learned
1. AI excels at mechanical refactoring: Moving code between files, updating imports, maintaining consistency—these tasks are perfect for AI assistance.
2. You still need architectural vision: Claude won't design your system architecture. That's your job. The AI implements the vision you provide.
3. Iteration is built into the process: Don't expect perfection on the first try. Plan for multiple rounds of refinement.
4. Clear instructions are force multipliers: Investing time in precise instructions saves time overall. Be explicit about patterns, naming conventions, and boundaries.
5. Trust but verify: Review all changes. Run your tests. The AI is a powerful assistant, not a replacement for developer judgment.
Key Takeaway
AI-assisted refactoring isn't about replacing developer expertise—it's about leveraging AI for mechanical work while you focus on design decisions. Claude handled the tedious parts of refactoring, reducing the time investment by 70% and letting me concentrate on architecture.
If you're sitting on technical debt but worried about the time investment, consider trying AI-assisted refactoring. Start small, be specific with your instructions, and maintain rigorous code review. The combination of human architectural thinking and AI mechanical execution is remarkably effective.
The future of refactoring isn't choosing between manual work and AI—it's learning to orchestrate both.