How Claude Helped Us Pay Off Our Code Debt
Discover how AI-assisted refactoring transformed a 40-60 hour code debt cleanup into a 4-hour task. A real-world case study on using Claude to tackle inconsistent error handling across an entire application.
Technical debt accumulates when development teams prioritize shipping features quickly over maintaining clean code. When building a platform like Open Source Cloud that needs rapid iteration to gather user feedback and attract new users, it is easy for code quality to slip. Recently, we faced this exact challenge and decided to tackle it head-on — with a little help from AI.
The Reality of Code Debt
Every development team knows the tension between shipping fast and writing clean code. When you need to attract users and gather feedback quickly, technical debt inevitably accumulates. In our case, we had been pushing features rapidly for the Open Source Cloud platform, and the codebase showed it. The debt was real, and it was time to pay it off.
The Problem: Inconsistent Error Handling
Our specific debt came in the form of inconsistent error handling throughout the application. Multiple developers had worked on the project over time, each bringing their own approach to handling errors. The result was unclear error messages presented to users, crashes without meaningful feedback, and situations where the client showed success while the server had actually failed. It was a mess that affected user experience and made debugging difficult.
Planning Phase with Claude
Using Claude Code within Cursor IDE, we took a systematic approach to the refactoring. First, we analyzed error handling across the entire application in stages — examining the client, backend, API, and interfaces separately. Claude helped document findings for each layer and created a comprehensive action plan. We incorporated our own ideas about custom error types into the strategy, letting Claude augment rather than replace our architectural decisions.
Execution: AI-Assisted Implementation
With the plan in place, we developed an 8-phase action plan with detailed todo-lists for each phase. Claude implemented the changes while we reviewed each commit, maintaining developer oversight to ensure code quality and alignment with the project vision. This collaborative approach meant we stayed in control while dramatically accelerating the actual implementation work.
The Results: 10x Efficiency Gain
The numbers speak for themselves. This refactoring work was estimated to take 40-60 hours if done manually. With Claude assistance, we completed it in approximately 4 hours. That is a 10-15x efficiency improvement on a task that developers typically dread and postpone indefinitely. The code is now consistent, the error messages are clear, and the technical debt is paid.
Key Takeaway
Paying off code debt is easier than ever with AI assistance. The key is taking it step by step with clear instructions and validating results one by one. AI does not replace developer judgment — it amplifies developer productivity. If you have been putting off that big refactoring project, now might be the time to tackle it.
Read the Original Post
This article is a summary of a more detailed post by Joel del Pilar. For the complete story with additional insights and details, read the original article on dev.to.