Claude vs. GPT-5: Fixing AI-Generated Code

8/10/2025

The Great AI Code-Off: I Used Claude to Fix GPT-5's Code & Here's What Happened

Alright, let's talk about the elephant in the room. We're all using AI to write code. Whether it's a quick one-liner, a full-blown function, or even an entire application skeleton, AI coding assistants have become the new normal. & honestly, it's pretty amazing. But here's the thing, it's not always perfect. In fact, sometimes it's downright broken.

This got me thinking. We've got these two titans in the ring right now: OpenAI's GPT-5 & Anthropic's Claude 4.1 Opus. Both are incredible, but they have VERY different reputations in the dev community. GPT-5 is the speed demon, the "vibe coder" that can spin up an entire app from a single prompt. Claude, on the other hand, is the meticulous artisan, known for its clean, well-structured, & reliable code.

So, I decided to run a little experiment. What if I used the flashy new kid on the block, GPT-5, to generate some code, & then brought in the seasoned pro, Claude, to clean up the mess? Would it be a match made in heaven, or a recipe for disaster? I had to find out.

The Setup: A Simple (But Deceptively Tricky) Task

I decided to start with a fairly common task for a web developer: building a simple e-commerce product page. I wanted a responsive layout, a product image gallery with thumbnails, a quantity selector, & an "Add to Cart" button that would, for the sake of this experiment, just log the product details to the console.

I fired up my code editor & gave GPT-5 a very straightforward prompt:

"Create a responsive product page using HTML, CSS, and JavaScript. It should have a main product image and a few thumbnails below it. Clicking a thumbnail should change the main image. There should be a quantity selector with plus and minus buttons, and an 'Add to Cart' button. When the 'Add to Cart' button is clicked, log the product name, price, and selected quantity to the console."

Round 1: GPT-5's "Vibe Coding" Masterpiece

As expected, GPT-5 was FAST. In what felt like seconds, it churned out a complete set of HTML, CSS, & JavaScript files. On the surface, it looked great. The structure was there, the elements were all present, & the code was neatly formatted. It even had comments! This is the magic of "vibe coding" that everyone's been talking about – the ability to go from an idea to a functional prototype in the blink of an eye.

But as I started to dig in, the cracks began to show. The JavaScript, while functional, was a bit… quirky. The event listeners were all global, there was no real data management to speak of, & the code was all in one giant

<script>

tag at the bottom of the HTML file. It worked, but it wasn't what you'd call "production-ready."

& then there were the subtle bugs. The quantity selector, for example, would happily let you go into negative numbers. The "Add to Cart" button worked, but it was hardcoded to a specific product name & price. If I wanted to add another product, I'd have to duplicate a lot of code. It was a classic case of what I've seen some developers call "AI slop" – code that looks good on the surface but is riddled with hidden issues.

The Human Element: The Unspoken Role of the Developer

Before I handed the code over to Claude, I want to take a moment to talk about the role of the developer in this new AI-powered world. It's easy to think that these tools will replace us, but my experience with GPT-5's code highlighted a crucial point: AI is a tool, not a replacement. As a developer, my job wasn't to write the code from scratch, but to act as a quality control engineer, a code reviewer, & an architect. I had to understand what the AI had produced, identify its shortcomings, & then figure out how to fix them.

This is where a lot of people get tripped up. They trust the AI's output without question, & that's a recipe for disaster. You absolutely have to review every line of AI-generated code as if it were written by a junior developer on their first day. The problem is, the code is often so convincingly written that unless you have a solid understanding of the underlying principles, you might not spot the flaws.

Round 2: Claude's Meticulous Refactoring

Now it was Claude's turn. I didn't just want to tell it to "fix the code." I wanted to see if it could understand the intent behind the code & refactor it into something more robust & scalable. So, I gave it a more detailed prompt:

"I have this product page code generated by another AI. It's a good start, but it has some issues. The JavaScript is all in one script tag and isn't very well-structured. The quantity selector allows negative numbers. The product data is hardcoded. Can you refactor this code to be more modular and maintainable? Separate the JavaScript into its own file, create a simple data structure for the product, and fix the bugs in the quantity selector. Also, make sure the code is well-commented and follows best practices."

The difference was night & day. Claude didn't just patch the bugs. It completely re-architected the JavaScript. It created a separate

product-data.js

file to hold the product information, a

main.js

file for the application logic, & it even created a simple "state" object to manage the currently selected product & quantity.

The quantity selector was fixed, with proper validation to prevent negative numbers. The event listeners were now more targeted & efficient. & the code was BEAUTIFULLY commented, explaining not just what the code was doing, but why it was doing it that way. This is where Claude's reputation for producing "cleaner, more reliable code" really shone through.

The Showdown: A Side-by-Side Comparison

So, who's the winner here? Honestly, it's not that simple. GPT-5 & Claude are two different tools for two different jobs.

GPT-5 is the ultimate rapid prototyper. If you need to get an idea off the ground quickly, or you're a non-technical user who wants to build a simple app, GPT-5 is your go-to. Its speed & versatility are unmatched. But you have to be prepared to deal with the rough edges.
Claude is the master craftsman. When code quality, maintainability, & correctness are your top priorities, Claude is the clear winner. It's the AI you want on your team when you're building a serious, long-term project.

The real magic, I found, was in using them together. GPT-5's speed for the initial draft, & Claude's meticulousness for the refinement, created a workflow that was both incredibly fast & produced a high-quality result.

A New Workflow: AI-Assisted, Human-Driven

This experiment has fundamentally changed how I think about using AI in my development process. It's not about replacing human developers; it's about augmenting their abilities. It's about letting the AI handle the boilerplate & the initial grunt work, so we can focus on the more complex, creative aspects of software development.

Think about it this way: GPT-5 is like having a team of hyper-caffeinated junior developers who can churn out code at an incredible rate. Claude is the senior developer who comes in, reviews their work, & turns it into something truly special. & you, the human developer, are the tech lead, orchestrating the whole process.

Beyond Code: The Implications for Business & Customer Interaction

This whole experiment got me thinking about more than just code. It's about the broader implications of AI in business. We're seeing a similar dynamic play out in the world of customer service & engagement.

On one hand, you have simple, scripted chatbots that can answer basic questions & handle a high volume of inquiries. They're the GPT-5 of customer service – fast, efficient, but not always the most nuanced.

On the other hand, you have more sophisticated conversational AI platforms. This is where a tool like Arsturn comes in. Arsturn helps businesses build no-code AI chatbots trained on their own data. This means the chatbot can provide truly personalized & context-aware customer experiences. It's not just about answering questions; it's about building meaningful connections with your audience.

Just like with our coding experiment, the best approach is often a hybrid one. You might use a simpler chatbot for initial triage, & then escalate to a more sophisticated AI, like one built with Arsturn, for more complex interactions. It's all about using the right tool for the right job.

The Future of AI in Development: A Collaborative Approach

So, what's the bottom line? Is GPT-5 better than Claude? Is one going to put the other out of business? I don't think so. The real takeaway for me is that the future of AI in development isn't about a single "best" model. It's about a collaborative ecosystem of tools, each with its own strengths & weaknesses.

The developer of the future won't be a lone coder, slinging code from scratch. They'll be a conductor, an orchestrator, a collaborator, working with a team of AI assistants to build amazing things. & honestly, I'm pretty excited about that.

Hope this was helpful! Let me know what you think. Have you tried a similar experiment? What have your experiences been with GPT-5 & Claude? I'd love to hear your thoughts in the comments below.