Every developer has faced it, opening an old codebase and instantly thinking, “Who wrote this?” Then, realising… it was you.
Legacy code slows teams down, introduces bugs, and makes scaling painful. But with AI, refactoring has become faster, safer, and even enjoyable. Here’s how I use AI to breathe new life into old codebases without breaking what works.
1️⃣ Understanding Legacy Code Quickly
Before refactoring, I first need to understand what the existing code actually does. AI summarises logic, dependencies, and potential bottlenecks in minutes.
💡 Prompt Example:
“Explain this legacy code in simple terms. Summarise what each function does and how they connect.”
This gives me a high-level view of the architecture, without diving into every file manually.
**2️⃣ Suggesting…
Every developer has faced it, opening an old codebase and instantly thinking, “Who wrote this?” Then, realising… it was you.
Legacy code slows teams down, introduces bugs, and makes scaling painful. But with AI, refactoring has become faster, safer, and even enjoyable. Here’s how I use AI to breathe new life into old codebases without breaking what works.
1️⃣ Understanding Legacy Code Quickly
Before refactoring, I first need to understand what the existing code actually does. AI summarises logic, dependencies, and potential bottlenecks in minutes.
💡 Prompt Example:
“Explain this legacy code in simple terms. Summarise what each function does and how they connect.”
This gives me a high-level view of the architecture, without diving into every file manually.
2️⃣ Suggesting Modern Syntax & Best Practices
AI can instantly rewrite code using modern standards, frameworks, or syntax, keeping functionality intact.
💡 Prompt Example:
“Refactor this ES5 JavaScript code into ES6 syntax using arrow functions and async/await.”
In seconds, I get production-grade code that follows today’s standards.
3️⃣ Breaking Down Monolithic Code
Many legacy systems have huge functions that do everything. AI helps split them into smaller, modular parts.
💡 Prompt Example:
“Refactor this function into smaller functions with single responsibilities. Keep logic consistent and add comments.”
Clean separation improves readability and makes future maintenance painless.
4️⃣ Identifying Redundant or Repetitive Code
Duplicate logic is the hidden debt in most projects. AI identifies redundancy and suggests consolidation.
💡 Prompt Example:
“Analyse this file for duplicate logic or unnecessary code. Suggest reusable helper functions.”
This instantly reduces complexity and future bugs.
5️⃣ Generating Test Cases After Refactor
After refactoring, I use AI to create test cases to confirm that everything still works.
💡 Prompt Example:
“Write unit tests for these updated functions. Include both positive and edge cases.”
Testing ensures the refactor didn’t accidentally break any functionality.
🎯 Final Thought
Refactoring used to feel like cleaning a warehouse in the dark. Now, AI acts like a flashlight showing what’s wrong, how to fix it, and how to make it better for the future.
Clean, modular, and maintainable code isn’t a luxury anymore. It’s what keeps teams shipping confidently, and AI makes it possible without losing weeks.