Nowadays, generative AI tools have evolved so much that they allow you to materialise almost any idea into concrete user interfaces, either websites or prototypes. In my case, I want to reflect on my use of v0 to prototype a company-designed AI tool.
For another class called Validation & Pivoting, we undertake an entrepreneurial project. In this context, we had to prototype our idea, and we could use AI tools to achieve a qualitative prototype. To optimise the use of v0, I previously transferred the main idea to ChatGPT which helped me write a precise prompt for v0 to create the prototype closest to the final idea. I was very impressed by how v0 manages to transform your idea given in basic words into a very detailed, user friendly and well thought prototype draft. Not only is this draft very close to what I imagined, but it is also very easy to use the chatbot included in v0 to make modifications and adapt your prototype toward its final version. It feels just like chatting with a designer or developer, except that it’s more of a monologue than a dialogue, and that the chatbot doesn’t take initiative or sometimes lacks creativity. Another major advantage is that it also gives you access to the actual frontend code, which you can inspect or even modify. It is also very helpful if you consider connecting a backend to this frontend, allowing you to turn your prototype into a concrete MVP.
It also comes with some drawbacks and limitations. Firstly, there is a risk of over-reliance on AI tools for designing user interfaces. Moreover, it does not perform full user simulations or test dynamic workflows. Lastly, the code sometimes needs to be verified, and the backend creation could be more optimised.
Generative AI tools are revolutionary to create user interfaces in minutes, but how much more transformative will they become once the corresponding backend can also be generated?
As a software developer, I have gotten my hands dirty in all different scales of code bases. I have built things as small as a todo list (aka everyone’s first coding project) and interned in a company that has built an enterprise solution, whose codebase is 1m+ lines of code. Although I have never used v0 directly, I do use vercel for deployment and CI/CD tools, and I think it is very interesting to see the stark contrast of attitudes between technical and non technical peopel when it comes to AIs ability to contribute to code. In my opinion, AI paired with a decent programmer is all you will ever need to get a program from 0 to 1. Whenever I am building a new feature or building on a new platform (eg: right now I am building https://kaducare.com and recently needed to create a chrome extension for a hospital demo) I’m almost never coding a single line–mind you I still read the code.
The issue, however, is when codebases start getting big and complex. Technical debt is a very real thing and there is a lot more to code than just the output. In my experience, even the smartest LLMs right now (Cladue, Gemini, ChatGPT) struggle to write code reliably within a big system, which puts a company like Vercel in a very interesting position because in addition to v0 they are trying to build AI agents that scan for bugs in code before they surface, and I am scepticle of the efficacy of that.
This is a fantastic, hands-on look at where these tools are right now. The real takeaway for me is your workflow, using ChatGPT to write a better prompt for v0. That’s the next level of skill with AI: it’s not just about using the tools, but about how you chain them together to get a better result.
You’re spot on about the limitations. It feels like we’re in this amazing “co-pilot” phase where the AI can build the frame of the house instantly, but you still need a human to check the foundation and do the wiring.
And your final question about the backend is the million-dollar one. Once an AI can generate a secure, scalable backend and connect it to the front end with a single prompt, the entire concept of an “MVP” will change forever. Great post!