I know — professional software engineers look at this title with contempt. It has been proven multiple times already that the quality and overall value of vibe-coded solutions is at least questionable, not to mention that deploying a half-baked app to production can cause more damage than actual profits.
It Is Not a Magic Wand
Let’s quickly review the most common concerns raised by the industry. Then I will tell you how I use vibe-coding and why I think this technique brings value.
Code Is Messy
First of all, AI-generated code can be messy, poorly structured, and hard to maintain or scale long-term. The codebase becomes unmaintainable very quickly. At some point, it is better to start from scratch rather than keep prompting over and over again to add one more feature to your “product.”
Debugging Is Hard
Vibe-coded software is almost never reviewed, or it is reviewed superficially. That’s an invitation for hidden bugs, security vulnerabilities, or inefficient logic. The bigger the codebase, the harder it is for an LLM to find a mistake and generate a reasonable fix without breaking anything else. At some point, eventually, you need to look at the source code, read, and understand it. The vibe-coding adventure hits a dead end.
Less Critical Thinking
An experienced vibe-coder can apply more advanced techniques like per-prompt code reviews, manual refinements of LLM outputs, and more careful reviews of bigger chunks of changes. With this approach, we can get quite satisfying results, and maybe even deliver a small feature or a tiny product. Everything would be good if we weren’t lazy by nature. In every answer that the LLM provides, it introduces some assumptions that we accept, because why not? It looks like a good idea. We stop thinking deeply about the problem we are solving and start relying on the LLM more and more to think for us. This is dangerous because our problem-solving skills are slowly degrading.
Why Is It Awesome Then?
How can vibe-coding be awesome, you may ask? Well, despite all that we discussed, I still see a huge potential in this approach.
Ideas Validation Becomes Cheap
I remember my beginning of using ChatGPT. When I realised it can write working software, I started flooding it with ridiculous prompts like “Create an e-commerce platform like Amazon,” or “Build a clone of Figma,” etc. My goal wasn’t to actually build a competition for these companies but rather to see where the limits for ChatGPT were. At one point, I realised that the LLM (gpt-3.5 back then) was an excellent tool for enabling experimentation. With a single prompt (or a few), I could implement an idea that would cost me an entire Saturday afternoon if coded manually.
My First Experiment
My first experiment with a well-defined goal was to train a simple neural network for image recognition. At the university, I learned how neural networks work, but I never really had time to play with more advanced examples. After a single prompt, ChatGPT generated a step-by-step guide on what libraries to install, how to download the model, and where to get a good training dataset from. I had some tiny issues along the way, but the LLM helped me to solve them. At the end, I ran the script, and the test image was correctly classified. Was the code production-ready? Absolutely not. Was it fun? Yes! It all took me 20 minutes.
Exploration Is Easy
It hit me. I was able to build an image classifier prototype without any past experience with machine learning tools. I knew the theoretical basics but nothing more than that. The vibe-coding technique is enough to lower the entry barriers for new areas. I am not saying that I could call myself a machine learning engineer, but exploring a new field has never been easier. That day I also vibe-coded some C++ and Arduino projects, just to bring back memories of good, old college days.
No More Boring Stuff
Trying out new things and learning is fun; doing the same thing over and over again is not. I am not able to count how many times I wanted to try a new Python or Node.js library in a bit more complex environment than a single-file script, but I resigned because setting up a proper project template was taking too much time. I don’t have this issue anymore. Testing Stripe integration? No problem. An LLM can help with setting up a full-stack app with the necessary services and a user interface.
Final Words
We have context management, spec-driven development, and agent frameworks nowadays. Despite that, quick prompting without prior planning isn’t dead. It rather complements more advanced techniques and can still provide value in many cases when used wisely. Happy vibe-coding!