AI Website Repair

AI helped you build the website faster. Now comes the bit that actually matters. Does it work properly? Not just “does it look okay in preview?” I mean: Do the forms send? Does it work properly on mobile? Is the page structure clear? Can Google understand it? Can AI search tools understand it? Is it fast enough? Is the checkout working? Are the tracking scripts firing? Are the links correct? Is the site secure? Does the content actually help someone decide? Because this is where a lot of AI-built websites fall over. Not because AI is bad. It isn’t. AI website builders are useful. They help small businesses, founders and agencies move faster than they could before. That’s a good thing. But fast doesn’t automatically mean finished. A website can look polished and still have broken forms, weak SEO, poor mobile layout, missing metadata, slow pages, bad accessibility, fragile code, or tracking that doesn’t work. And most business owners won’t know until something goes wrong. That’s the gap. AI can help you build faster. But you still need to test the thing properly before real customers rely on it. Because the goal isn’t just to launch a website. The goal is to launch something that works.

A customer asked a good question this week. They’ve got a custom web application that runs a big part of their business. Projects. Schedules. Documents. Site notes. Invoices. They asked whether it could connect to their accounts software. Fair question. The first thing to check is simple: Does the accounts software have an API? Because if it does, there may be a sensible way to connect the two systems. If it doesn’t, or if it locks the data away, or if it only allows limited access, then AI doesn’t magically remove that problem. The reply was interesting: “Can’t AI just do it now?” And that probably sums up where a lot of people are with AI at the moment. AI is becoming incredibly useful. It can help plan integrations. Read documentation. Write code. Spot issues. Speed up development. Help test things. But it doesn’t remove the need for access, permissions, data structure, security, logic, and proper system design. It can’t make closed software open. It can’t safely connect systems when the underlying route doesn’t exist. It can’t guess business rules that haven’t been defined. That’s the bit that matters. AI can speed up the work when the foundations are there. But the plumbing still matters. The architecture still matters. The boring technical details still matter. AI is a powerful tool. It’s not a magic wand.

Stop! Yes AI can build a website. But can it take a high-end agency design and turn it into clean, responsive, production-ready front-end code? That’s where things gets messy... I’ve been testing this properly on a real project. Not a fake landing page. Not a pretty AI demo. A proper website design supplied as flattened PNG concepts, originally created in Illustrator, with complex clipping paths, masks and visual details that are hard to extract cleanly. The job sounds simple on paper: Take the design. Recreate it as clean HTML and CSS. Make it responsive. Keep it close to the original. Don’t butcher the details. In reality, AI struggled. One tool wasn’t enough. A single prompt wasn’t even close. The best results came from using three tools together: ChatGPT for thinking, structure and problem-solving. UXPilot for layout direction and visual interpretation. Cursor for the actual front-end build and refinement. Even then, the only way it started working was painfully slowly. One section at a time. Header. Hero. Product cards. Content blocks. Mobile layout. Spacing. Typography. Responsive fixes. Refinement pass after refinement pass. And here’s the honest truth. At this level, it has most definitely taken longer than giving the job to a good front-end developer. That doesn’t mean AI is useless. Far from it. It means the people shouting “AI can build websites in minutes” are usually talking about simple websites, loose designs, low expectations, or output that only looks fine if you don’t inspect it too closely. For high-end web work, the hard part isn’t generating code. The hard part is judgement. Knowing what’s wrong. Knowing why it’s wrong. Knowing what to fix first. Knowing when close enough isn’t good enough. Knowing how design, UX, responsive behaviour, accessibility, performance and code quality all fit together. AI can speed parts of that up. But it doesn’t replace knowing what good looks like. That’s the bit I think a lot of people are about to learn the hard way.

Really useful point. With websites, I’d go even smaller than one big prompt or even one whole page at a time. Once the UX and design direction are clear, build section by section. Hero. Navigation. Trust strip. Cards. Forms. Mobile states. Calls to action. Each section gets a better brief, a cleaner check, and fewer things can quietly go wrong. That’s where we’re seeing the best results with AI at the minute. Not asking it to guess the whole thing in one go, but guiding it properly, one part at a time.

One criticism we keep seeing about AI-built websites is: “They all look the same.” Fair. But let’s be honest. Generic websites didn’t start with AI. AI has just made the problem more obvious. Plenty of businesses have been sold “bespoke” websites for years that were really the same basic layout with different colours, different images and a few swapped sections. Same structure. Same flow. Same lack of thought. So maybe the real issue isn’t AI making websites generic. Maybe the real issue is building websites without proper strategy, structure, UX and conversion thinking. AI can definitely create generic websites. But so can humans. That’s the uncomfortable bit.

This is such a good business lesson. Most businesses don’t need more things. More services. More offers. More pages. More tools. More campaigns. More half-finished ideas. They need fewer things done properly. Focus is boring until you see what it fixes. Clear offer. Clear message. Clear audience. Clear website. Clear next step. AI makes it even easier to create more noise now. The harder skill is knowing what to cut.

AI search is changing where answers come from. Not just websites. Not just brand blogs. Not just polished sales pages. AI tools are pulling more from places where real people are talking. Reddit. LinkedIn. Forums. Comments. Community threads. And that makes sense. Because those places often show the stuff websites leave out. The website says the tool is easy. The comments tell you where people got stuck. The sales page says everything works. The forum thread tells you the form broke, the mobile layout failed, or the integration didn’t behave properly. For AI-built websites, this matters a lot. The useful conversations are not just: “Look what I built in 10 minutes.” They’re things like: What broke after launch? What did the AI miss? What needed checking? What failed on mobile? What looked finished but wasn’t? That’s where the real value is. AI website builders are useful. They help people move faster. But the honest lessons usually show up after the first version is built. In the testing. In the fixing. In the real-world use. The businesses that win won’t just be the ones publishing polished content. They’ll be the ones answering real questions clearly, showing what they know, and being useful where people are already looking for help. AI is the tool. The thinking still matters.

Exactly. People were saying the same thing around 2002 when Photoshop became widely available. Suddenly everyone with Photoshop was apparently a designer or photographer. Then, after a while, the obvious truth came back round. Owning the software doesn’t make you skilled. Same with AI. It can help more people build things, and that’s a good thing. But having a tool that can generate code doesn’t mean you understand what makes a website, app or business system actually work properly.

The best AI prompt I ever wrote was a question, not a command. Most people prompt like this: "Write me a post about email marketing." That gets you generic output. Stop telling AI what to make. Start asking it what you're missing. Here are 11 questions I now use instead of commands: 𝗧𝗼 𝗳𝗶𝗻𝗱 𝘄𝗲𝗮𝗸 𝘀𝗽𝗼𝘁𝘀: → "What's the single biggest reason someone would stop reading this?" → "Where does my argument get lazy or hand-wavy?" → "What would a harsh editor cut first?" 𝗧𝗼 𝘀𝗵𝗮𝗿𝗽𝗲𝗻 𝘁𝗵𝗲 𝗮𝗿𝗴𝘂𝗺𝗲𝗻𝘁: → "What am I assuming that might be wrong?" → "What's the strongest counterargument to this?" → "If this is the answer, what's the better question I should be asking?" 𝗧𝗼 𝗰𝘂𝘁 𝘁𝗵𝗲 𝗳𝗹𝘂𝗳𝗳: → "What am I saying twice that I only need to say once?" → "Which paragraph adds the least value?" → "If I had to cut this in half, what goes first?" 𝗧𝗼 𝗽𝗿𝗲𝘀𝘀𝘂𝗿𝗲-𝘁𝗲𝘀𝘁 𝗶𝘁: → "Read this as someone who disagrees with me. What would they say?" → "What's the one thing this piece is trying to say? Is it obvious?" Commands spit out content. Questions sharpen your thinking. The people getting the most from AI aren't prompting faster. They're asking sharper questions. ♻️ Repost if this was useful. 🔔 Follow me for more on AI, marketing, and copywriting.

This is worth sharing. There is a big difference between using AI to generate bits of work and using AI inside a proper system. Prompts are useful. But prompts alone get messy fast. For website and app builds, the stronger approach is: Set the rules. Define the structure. Document decisions. Create checks. Review outputs. Test before launch. Keep improving. That is where AI becomes genuinely useful, not just fast. Same applies to AI-built websites. The first version might look impressive, but without structure underneath, things can get fragile quickly. AI is the tool. The thinking still matters.