Why GPT-5 Fails at Frontend & How to Make it Work

8/12/2025

So, You Think GPT-5 Sucks at Frontend Coding? Here's the Real Deal.

Alright, let's talk about GPT-5. The hype was HUGE, right? OpenAI dropped it, calling it their best coding model yet, with demos showing it spinning up entire applications from a single sentence. They even said developers preferred its frontend code 70% of the time in their own tests. But if you're a frontend developer who's actually tried to use it for, you know, work, you might have a slightly different story.

Maybe your experience has been less "mind-blowing AI partner" & more "frustrating intern who keeps breaking the build." You ask it to create a simple modal, & it puts the close button in a weird spot or forgets to display the image. You tell it to fix a bug, & it ignores your instructions or, even worse, introduces a whole new set of problems. I've seen the threads on Reddit & the OpenAI forums; a lot of people are feeling like it's a step backward, even from GPT-4.1.

So, what's going on? Is GPT-5 actually bad at frontend coding, or is there something else at play? Honestly, it's a bit of both. Here's the thing: the slick demos are great, but they don't reflect the messy reality of production-level frontend development. Our job isn't just about one-shot generating a pretty UI. It's about responsive design, accessibility, state management, integrating with legacy code, & writing something that another human can actually maintain. That’s where things get tricky for a large language model.

But it's not a lost cause. Turns out, getting GPT-5 to be a useful coding assistant is less about what you ask it & more about how you ask it—and knowing its limitations. I've been in the trenches with it, & I've figured out a few things that can make a world of difference.

Why It Feels Like GPT-5 is Failing at Basic Tasks

Before we get into the fixes, let's break down why it seems to be dropping the ball. It's not always just "the AI is dumb." The issues are a bit more nuanced.

1. The "One-Shot Wonder" Problem

GPT-5 is AMAZING at generating something from nothing. Those "build a Netflix clone from scratch" prompts are its bread & butter. The problem is, that's not how most of us work. We're usually working within a massive, existing codebase with its own weird dependencies, custom frameworks, & years of technical debt.

AI models, even GPT-5, can really struggle with context switching & managing the state of a complex application. They might "forget" a crucial piece of information you gave them five prompts ago, leading to code that breaks existing functionality. It's like trying to explain a complex project to someone with short-term memory loss.

2. The "Hype vs. Reality" of UI Generation

OpenAI's cookbook shows GPT-5 creating "production grade outputs" from a single underspecified prompt. But developer reviews tell a different story. One user on DEV Community asked for a simple modal & got something where the "X" blocked the discount banner & the image didn't even show up. Another developer on YouTube found the UI it generated for a simple app to be broken & less aesthetically pleasing than what Claude produced.

The model can generate the structure of a component, but the fine-tuning—the part that makes it look good & work flawlessly across devices—often still requires a human touch. It produces skeletal outputs that need polishing.

3. You Might Be Using the Wrong "Version" of GPT-5

This is a BIG one. It turns out, "GPT-5" isn't just one monolithic thing. There are different versions or modes, like

gpt-5

gpt-5-mini

, &

gpt-5-nano

, plus a "thinking" mode that's designed to reason more deeply through complex prompts. If you're using the base model or a "fast" version for a complex task, you might be getting lower-quality results because it's optimized for speed, not deep thought. One user on ZDNET got terrible results from the base model on coding tests but was impressed by its ability to analyze a whole GitHub repo when prompted correctly. This routing between different internal models can lead to inconsistent experiences.

How to Actually Get GPT-5 to Write Good Frontend Code

Okay, enough complaining. Let's get to the good stuff. You can absolutely bend GPT-5 to your will & make it a powerful ally. It just takes a bit of strategy.

H2: Master the Art of the Prompt (Seriously)

This is the most important skill for working with any LLM. Vague instructions get vague results. Here are some prompt engineering techniques that have worked for me & others.

Context First, Ask Second: This is a simple but powerful change. Start your prompt by giving it all the necessary context—paste the relevant code snippet, the CSS file, the error message, whatever it needs. THEN, at the end, make your request. This helps the model ground its response in the specifics of your project.
Break Down Complex Tasks: Instead of asking it to "build the entire user profile page," break it down into smaller, more manageable chunks.
1. First prompt: "Here's my data model. Generate the HTML & CSS for the user avatar & information section."
2. Second prompt: "Now, create the React component for the 'Edit Profile' button & the modal that opens when it's clicked."
3. Third prompt: "Here's the code for the modal. Add the form fields for username & bio, along with a 'Save' button." This approach helps the model stay focused & produces much more reliable code.
Use the "High-Specificity Technical Mode" Prompt: A writer for ZDNET, after being disappointed with GPT-5's coding abilities, developed a fantastic prompt to force the model to be more detailed. It's a game-changer. Add this to the beginning of your query:
1*High-Specificity Technical Mode: ***In your answer, combine complete high-level coverage with exhaustive implementation-level detail. Always name exact constants, functions, classes, hooks, option names, database tables, file paths, and build tools where possible, quoting them exactly from the code or material provided.

This essentially tells the model to stop being lazy & give you the kind of detailed, actionable information a real developer would need.
Tell It to "Think Step by Step": For complex logic or debugging, start your prompt with "Let's think step by step." This encourages the model to use its deeper reasoning abilities & lay out its thought process, which often leads to better solutions & helps you spot errors in its logic.

H2: Leverage Different Models & Settings

Don't just stick with the default chat. Explore the different options available to you.

Use "Thinking" Mode for Complex Problems: If you have a particularly thorny bug or a complex architectural question, try using the "thinking" or "Pro" version of the model if you have access. It's slower, but it's designed to handle the kind of multi-step reasoning that coding requires.
Use "Fast" or "Mini" for Simple Stuff: If you just need to write a simple function, convert a JSON object, or get a quick Regex pattern, the faster, cheaper models are perfect. Don't waste the 'thinking' model's brainpower (or your money/time) on simple tasks.

H2: Don't Be Afraid to Use Other Tools

Here's a secret: you don't have to be exclusively loyal to GPT-5. The smart developer uses the right tool for the job.

For Refactoring & Precision, Try Claude: Many developers are finding that while GPT-5 is great for generating new things, Anthropic's Claude is superior for editing & refactoring existing code. It seems to be better at understanding complex project scope & making precise, surgical changes without breaking unrelated things. Some benchmarks even show Claude Opus 4.1 outperforming GPT-5 on specific coding tests like SWE-bench.
For UI from Images, Check Out Gemini: Google's Gemini is another strong contender, especially in the multimodal space. Tools like
1Gemini-UI-To-Code
are being developed that can take a screenshot or even a Figma design & generate the frontend code for it. It's incredibly powerful for quickly scaffolding a UI that you can then refine.

H2: When to Use a Specialized Solution (Like Arsturn)

Sometimes, what you're trying to build has already been perfected by a specialized tool. Let's say you're building a new landing page & you want to add an interactive chat experience to engage visitors & answer their questions instantly.

You could spend hours prompting GPT-5 to generate the frontend & backend logic for a chatbot, wrestle with its buggy output, & then try to hook it up to your knowledge base.

Or... you could use a tool like Arsturn.

Here's the thing: for tasks like customer engagement & support automation, a general-purpose LLM is overkill & often not the right tool for the job. This is where contextually appropriate business solutions come in. For example, Arsturn helps businesses build no-code AI chatbots trained on their own data to boost conversions & provide personalized customer experiences. Instead of fighting with generated code, you use a simple platform to create a custom AI chatbot that can provide instant customer support, answer questions from your website's content, & engage with visitors 24/7. It's a perfect example of using a targeted solution to solve a specific frontend challenge more efficiently than a general-purpose AI ever could.

The Final Word

So, does GPT-5 suck for frontend? No, but it's not the magic bullet we were all hoping for. It's an incredibly powerful tool, but like any tool, you need to know how to use it properly. The days of just typing a lazy question into a prompt window & getting perfect results are over.

The key is to be a good collaborator. Provide clear, specific context. Break down your problems. Use the right settings for the job. & most importantly, don't be afraid to bring in other tools—whether it's Claude for a tricky refactor or a specialized platform like Arsturn for building a high-quality chatbot.

By moving from a simple user to a sophisticated prompter, you can get past the frustrating failures & start tapping into the real power of AI-assisted coding.

Hope this was helpful. Let me know what you think & if you've found any other tricks that work