AI Coding with Windsurf: A New Approach to TDD

AI Coding: How Windsurf and TDD Fix the Biggest Pain Points

AI-driven development is evolving fast, but it still has some frustrating gaps—especially when it comes to coding at scale. If you’ve ever tried to use ChatGPT to build out a feature, you’ve probably run into the same issues I have: constant copy-pasting, lack of code context, and struggles with multi-file edits. It can feel like AI is more of a productivity sink than a boost.

That’s where Windsurf comes in. It’s an AI-focused IDE that reimagines coding with AI-first workflows. Over the past few weeks, I’ve been diving deep into Windsurf, testing its capabilities, and refining a workflow that actually makes AI feel like a true development assistant.

In this post, I’ll break down:

Why traditional AI coding tools fall short
How Windsurf changes the game
Why Test-Driven Development (TDD) is essential for AI-assisted coding
Some real-world examples of building a feature from scratch with AI

The Problem: Why AI Coding Has Been Painful

When using AI tools like ChatGPT for coding, I’ve run into four major problems:

1. Lack of Context

AI models don’t inherently understand your codebase. Every time you ask for help, you have to manually copy-paste files, explain existing patterns, and set up the right context. This breaks the flow state and wastes a ton of time.

2. Multi-File Editing Is a Mess

Most features touch 10+ files. Managing AI-generated changes across multiple files is tedious. You end up bouncing between ChatGPT, your editor, and your terminal—copying, pasting, and debugging in a loop.

3. Debugging Feels Inefficient

AI doesn’t inherently learn from its mistakes. When an AI-generated snippet doesn’t work, you have to manually feed error logs back in and hope it corrects itself. This back-and-forth often cancels out the time savings AI is supposed to provide.

4. AI Lacks a Feedback Loop

Good developers don’t just write code—they test it. Without an automated way for AI to iterate and self-correct, you’re stuck as the middleman, manually debugging every failed output.

Enter Windsurf: AI Meets the IDE

Windsurf is built to solve these problems. It’s an AI-first editor that integrates directly with your repo, making AI a seamless part of the development workflow rather than a clunky add-on.

Key Features:

Repo Awareness – AI has the full context of your project, so no more endless copy-pasting.
Command Execution – Windsurf can suggest and run commands, handle migrations, model generation, and more.
Live Code Awareness – It tracks your changes in real-time, reducing the need for redundant explanations.
Multi-File Editing (Cascade Mode) – AI can update, add, and remove files across your project in one go.
Memory & Global Preferences – You can define project-wide coding standards and TDD preferences, which AI follows automatically.
Image Upload for Debugging – Take a screenshot of a UI issue, upload it, and let AI analyze it.

I found that once I combined Windsurf with a TDD workflow, things really clicked.

When I first tried Windsurf without TDD, I ran into the same AI-related frustrations—bugs, regressions, and endless fixes. However, once I started using a TDD-first approach, the experience completely changed.

TDD + AI = A Better Feedback Loop

AI Starts by Writing the Tests – Before it even touches the implementation, AI generates the test cases. This lets you verify if it’s approaching the feature correctly.
AI Iterates Until Tests Pass – Instead of blindly generating code, AI has a clear definition of success and keeps refining until all tests go green.
Catches Accidental Regressions – When AI modifies your codebase, TDD ensures it doesn’t break existing functionality.
Refactoring Becomes Safer – Once all tests pass, you can have the AI refactor the code confidently.

With this workflow, AI coding feels way more structured and useful. It’s like giving the AI a roadmap rather than letting it drive blind.

AI Building a Feature in 15 Minutes

To put Windsurf to the test, I used it to build out a simple asset management system with:

An Assets index page (filterable/searchable)
New/edit Assets forms
A show page
Fully styled with working components

Step 1: Defining the Requirements

First, I recorded a Loom walkthrough of the existing app we were hoping to rebuild, then used ChatGPT’s o1 model to extract detailed development requirements. This is a game-changer—it means AI gets a structured spec instead of me having to manually write the specs out.

Step 2: Letting AI Take the Wheel

I fed the requirements into Windsurf, and it started:

✅ Writing the specs using RSpec

✅ Creating the model, controller, and view files

✅ Running the tests and debugging itself until all tests passed

Step 3: Iterating & Debugging

When an issue popped up (like an incorrect enum syntax for Rails 8), the AI detected the test failures, debugged the issue, and reran the specs until everything worked.

Total time: ~15 minutes.

That’s insane compared to manually writing the same code from scratch. I estimate that it would have usually taken me 6-8 hours to build the same features with tests by hand.

Key Takeaways & AI Coding Best Practices

If you want to get started with AI-assisted TDD, here are some things I learned:

Work in Manageable Chunks – Don’t overwhelm AI with too much at once. One feature/page at a time works best.
Be Extremely Detailed in Requirements – AI is only as good as the instructions you give it. Just like any developer, the AI needs detailed requirements to get it right.
Use a UI Design System – AI works much better with a structured component library like Tailwind or a predefined style guide.
Commit Often – If AI goes off the rails, having rollback points is crucial.
Throw Away Bad Code – If AI-generated code isn’t great, don’t waste time fixing it. Just tweak the prompt and retry.

The Future of AI Coding with Windsurf and TDD

Windsurf isn’t perfect, but it’s really really good. It’s the closest thing I’ve seen to an actual AI pair programmer. By combining it with a solid TDD approach, you can significantly cut down development time while maintaining code quality.

We’re still in the early stages of AI-first development, but tools like this make it clear that the future of software engineering is going to look very different.

If you’re interested in trying this out, my recommendation is to:

Start with a small side project to get comfortable
Test different AI models (Claude Sonnet, GPT-4o, etc.) to see which works best
Experiment with different prompting techniques to refine your workflow

AI-assisted development isn’t replacing engineers—it’s amplifying what we can do. The more we embrace tools like Windsurf and refine our workflows, the more we can focus on solving real problems instead of writing boilerplate code.

Want to learn more about advanced AI tooling or leverage our expertise, contact one of experts today!