🤖 Refactoring GraphQL with AI Coding Assistants: A Real-World Use Case

AI coding assistants are everywhere, but much of the content focuses on simplistic, contrived demos. “Hey AI, make me a to-do list app!” Of course, greenfield projects work like magic. The real test is applying AI to large, messy, real-world codebases that have evolved over time. That’s what I did while refactoring a legacy GraphQL API to support a new database schema without breaking the public interface.

This is how AI coding assistants work in practice: useful, but not magic. Here’s how I structured the process to make them work effectively.

The Challenge: Schema Fixes Without Breaking the Frontend

The goal was to fix deficiencies in an existing survey system schema that were holding back progress. But there was a catch: the frontend React developers couldn’t be derailed from their existing work. That meant preserving the GraphQL API interface while completely restructuring the underlying schema.

On top of that, the codebase was old, large, and undocumented, built by a previous team with inconsistent standards. No easy wins here.

Step 1: Define the Ideal Schema

Before touching any code, I manually designed the ideal schema based on current and future needs. This set a clear target for all changes.

Step 2: AI-Assisted Implementation Planning

A coding assistant (Claude 3.5 Sonnet via Cursor) was given a structured prompt outlining:

• The current and future state of the schema
• The need to maintain API compatibility
• The backend services impacted

It produced an initial high-level implementation plan, which I iterated on. AI won’t always get this perfect on the first try, but it accelerates structured thinking.

Step 3: Implement & Validate the New Schema

Once we had a vetted plan, I manually implemented the new schema in isolation, ensuring:

• Built the new database objects (ActiveRecord objects in this case)
• Validated attributes and relationships
• Implemented queries and data validation
• Wrote unit tests

Now came the detailed part: mapping the new schema without breaking anything.

Step 4: AI-Driven Query Mapping

Since the frontend relied on GraphQL queries that referenced the old schema, I used an AI-assisted pipeline to:

1. Scan the React app to locate every GraphQL query touching the affected schema fields.
2. Extract query parameters and store them in a structured “plan file.”

This created a map of dependencies, ensuring nothing slipped through the cracks.

Step 5: AI-Guided Backend Refactoring

With the plan file in place, I tasked the AI assistant with analyzing the Rails GraphQL API:

• Identifying where each GraphQL query was defined
• Mapping which GraphQL types were impacted
• Generating an implementation plan for incrementally updating queries to match the new schema

Step 6: Fine-Tuning the Migration Plan

Before running anything, I added manual replacement rules to the plan file:

• Direct class and attribute name swaps (e.g., Apple -> Orange)
• Schema transformations (where more complex rewrites were needed)
• Example snippets for AI-generated suggestions

Step 7: AI-Assisted Code & Test Generation

I fed the refined plan into Cursor (Claude 3.5 Sonnet) and tasked it with:

• Refactoring GraphQL query resolvers incrementally.
• Writing missing tests (huge testing gaps existed in the original implementation).

AI excelled at scaffolding tests and reducing code-hunting time, but was less reliable in fully rewriting complex query logic. Still, the time saved on searching through a massive codebase made it worth it.

The Results: A Hybrid AI/Human Approach

By combining AI-powered dependency mapping, automated planning, and test generation with manual architecture and validation, I successfully:

• ✅ Migrated the GraphQL API to a new schema without frontend disruption.
  ✅ Used AI to eliminate hours of code searching and documentation gaps.
  ✅ Applied targeted refactoring instead of risky full rewrites.
  

Final Takeaways

• 1️⃣ AI isn’t a magic bullet, but it’s a fantastic tool for structuring large-scale refactors.
  
• 2️⃣ Manual oversight is still critical—especially in high-stakes migrations.
  
• 3️⃣ AI is best used for accelerating repetitive tasks: scanning, planning, scaffolding tests.
  

🔹 Have you used AI for refactoring in real-world projects? How did it go?