Beyond the Prompt: What AI Design Tools Need to be Successful

My first experience with vibecoding felt like packing a carry-on after chugging a thermos of cold brew concentrate. For the uninitiated (involving either vibecoding or cold brew concentrate) it’s lightning-fast, and easier on the digestive system.Â
It was also a little thrown-together, like when you forget to bring socks or a fifth-extra pair of underwear.
What it’s Like Relying on Vibecode
The team’s task was simple enough: Create a signup workflow that allows users to open, modify, or cancel an account without having to contact customer service. We had a detailed, documented scope that informed our prompt, as well as image files of the project’s current user interface.Â
At first, the drafts were promising. We prototyped and coded an initial mockup within mere hours. Work that used to take days was seemingly completed in minutes.
It was a great start, but then the cracks started to show in the system. A missing confirmation page here. A misinterpreted design style there. Body text colors sometimes matched the background and broke accessibility guidelines. And why, despite my dead silence, did the button text turn purple all of a sudden?
What’s Missing from AI Design Tools?
In the end, and with some significant prompt-chaining, our vibecoded prototype did a great job of nailing the main actions. But we still needed to show how it fit in the project’s actual development environment, something completely bifurcated from the prototyping experience. It didn’t matter that we had plenty of visual and textual documentation — the output just wasn’t 100% right.
If you’ve vibecoded anything before, then you’ve been there. Prompt. Output. Re-prompt. Re-output. Back and forth, we tango with our LLMs as our fingers dance along the keyboard.Â
In fact, many designers and programmers still find that prompting takes more time than the hands-on action that they’re used to performing. A July study from METR, an AI research nonprofit, found that surveyed developers took 19% longer than they initially estimated to complete a coding task using AI code editors.
To be clear, METR isn’t technophobic. They’re former OpenAI and Anthropic employees, among other experts in this field.
The study is a microcosm of why, despite the sheer power of these AI tools, many designers’ perceptions of them are so lukewarm. They have the capability to speed up mind-numbing busywork, but also make more of it if our prompts aren’t seemingly perfect.
I’m not ditching AI in my workflows. We’re building AI tools and training new AI users day in, day out. But there’s a swath of other less-popular tools and methods that can improve both the design and development phases, all while boosting efficiency and maintaining good work.Â
What Do AI Tools Need to Be Scalable for Designers?
As a designer, I’m not worried about AI in a general sense. I’ll try anything that helps me work more efficiently if it’s not sacrificing quality.
My concern is that many of the most popular AI tools are lacking when it comes to designing interfaces. And it’s because they’re not designed for … well, design.
Here’s why: sometimes a prompt isn’t the right tool for the occasion.Â
Many designers and developers in this industry are having those thoughts right now. Could I make the adjustment quickly with a prompt, or could I have made the change faster using Figma design tools?Â
This is precisely where major LLMs, ChatGPT and Gemini in particular, fall short. “The Prompt”, this monolithic AI interface, might feel faster or intuitive for some tasks, but not for others.
Why file down a nail with a hacksaw when you can use the back of a hammer to pull it out? That exact sentiment is why The Prompt should be just one of many tools in our AI toolbelt.Â
What Water Bottles Can Teach Us About AI Tool Design
I LOVE versatility in utility. I think a lot of UX folks do. I’ll bet the guy who invented convertible pants did, too.Â
I’ve also accrued dozens of water bottles throughout the years. I’ve got steel ones, Nalgenes, squeeze ones, soft flasks for 15-mile runs, coffee thermoses — I’m set for life, folks. But my fiancĂ©e has an Owala, and I think it’s a designer’s dream.Â
If you’re not familiar with the concept, Owala’s line of FreeSip bottles have both a wide-mouth and a straw to accommodate swillers and sippers alike.
And while I can’t justify purchasing another water bottle ever again, it doesn’t stop me from thinking about 50 ways to torch those babies for an excuse to buy one of these bad boys.
Why is that? It’s just another example of the Robustness Principle playing out in the marketplace.
What is The Robustness Principle?
Back in 1979, when we were developing the first modern Internet frameworks, an innovative computer scientist named Jon Postel wrote that programs “should follow a general rule of robustness: be conservative in what you do, be liberal in what you accept from others.”
The Robustness Principle, or Postel’s Law, verbalized that stronger-built systems were capable of handling a wide array of inputs and flexible with errors. It also laid out a framework for designing any user experience, not just computer programs.
The gist is simple: Multiple users will find differing and preferred methods to achieve a given solution.
This is everywhere in the everyday world. Cars have volume buttons on both the dashboard and the steering wheel. Grocery store checkouts accept credit cards, cash, and sometimes mobile payments. In our work, we build websites to accommodate screen sizes that range from iPhones to televisions. Keyboards have shortcuts.Â
So, back to the water bottles. Owala’s design works so well because it follows the Robustness Principle. You’ve got a straw if you want to sip, you’ve got a wide-mouth opening if you want to chug. And unless you prefer to hydrate via osmosis, everyone wins.
Can the same be said of our AI design tools? Sometimes.
How AI Tools Can Improve Design Workflows
The days of continuous, asynchronous design and development are already here. If we’re going to effectively bridge that gap and optimize our workflows, then The Prompt may be an effective means of getting an expert designer 50 – 70 percent there in record time.
But how do we push past the other 30 percent? If we learned anything from the Robustness Principle, we know that sometimes another method is needed to make those “perfection” tweaks so we can get to a comprehensive design review.
Model Context Protocols (MCPs), which allow us to connect AI tools to one another to share data, are one step to the solution. But ultimately, what designers need are more nuanced tools that can handle it all. MCPs for connecting data, The Prompt for brainstorming, and our trusty toolbars for all the tiny details.
There’s already some promise on this front. I see a lot of potential in Figma’s new tool, Make, which incorporates both prompting and traditional design software inputs. It also works off your project’s specified design system to stay visually consistent between iterations. It’s still in its infancy, but the road ahead looks bright.Â
UXPilot, another high-fidelity AI wireframing tool, takes a similar approach to hands-on-and-off design. As far as LLMs go, Claude can also integrate with Figma to export finished design files as code that’s suitable for prototypes, and maybe even full sites for smaller projects.Â
Ultimately, this should demonstrate that AI efficiencies come from thoughtful planning, practice, and iteration — not just moving fast and breaking things.Â
It’s the kind of strategic planning and understanding that allows us to excel at Mindgrub. Because vibes are one thing; results are another. If you’re looking for both, we know how to get you there.