Architecting and Refactoring with AI

As software projects mature, they inevitably evolve. Business requirements change, product focus shifts, and the names and structures we chose early on may no longer fit. This leads to technical debt and makes the codebase harder to understand. Addressing this often requires large-scale refactoring, a task that has traditionally been one of the most tedious and high-risk activities in software development. Manually changing a core concept across an entire project is slow, error-prone, and can introduce subtle bugs that are difficult to find.

This is where an AI-first code editor fundamentally changes the game. With Cursor, we can transform refactoring from a manual chore into a strategic, AI-assisted process. We can use the AI as a partner to architect the change, delegate the complex multi-file execution, and verify the results with confidence.

To put this into practice, we will tackle a classic refactoring challenge: renaming a core model in our “NoteIt” application from Note to Document to better align with new business requirements. We will follow a professional, three-phase workflow. First, we will architect the plan, second, we will execute the change, and finally, we will verify the result.

Phase 1: Architecting the refactor (the plan)

Before we change a single line of code, we must understand the full scope of our task. Large-scale refactors are complex, and a professional workflow begins with a solid plan. In this phase, we’ll use AI as a strategic partner to analyze the impact of our proposed change.

Let’s suppose that our “NoteIt” application has evolved. The business requirements have shifted, and “Document” is now a more accurate term for our core concept than “Note.” Our task is to rename this concept everywhere in the codebase. This is the “architecting” phase of the refactor, where we identify every part of the system that will be affected. ...