I have spent the last few months watching the rise of "vibecoding." For those who haven't seen the term, it is the act of describing a feature to an LLM, hitting enter, and pasting the result until the app feels right. It is coding by vibe. No architecture, no manual typing, just a loop of prompts and refreshes.
At first, it feels like a superpower. You can stand up a working prototype in twenty minutes that would have taken me two days five years ago. But as an experienced developer, I can tell you that the "vibe" eventually hits a wall. That wall is called technical debt, and it arrives much faster when you are vibecoding.
The Illusion of Progress
When you vibe-code, you are optimizing for the visible surface. The UI looks great. The button triggers the API call. The data shows up on the screen. Because it works, you assume the underlying logic is sound.
But LLMs are probabilistic, not deterministic. They don't understand your business logic; they understand patterns in training data. If you don't deeply understand the code being generated, you are essentially outsourcing your mental model of the application to a black box.
I recently reviewed a project where the developer used this approach. The app worked perfectly during the demo. However, once we looked at the state management, it was a disaster. There were three different ways of handling the same piece of data, leading to race conditions that only appeared when the network slowed down. The "vibe" was good, but the engineering was non-existent.
Where Vibecoding Fails
There are three specific areas where relying on the vibe will break your project:
Edge Case Blindness: LLMs are great at the happy path. They are mediocre at the "what if the user uploads a 50MB file while their session expires" path. If you aren't writing the tests or thinking through the failure states, you are building a house of cards.
Dependency Hell: AI often suggests libraries that are outdated or slightly incompatible. When you just paste the code, you might miss the fact that you just added a heavy dependency for a task that could be solved with five lines of vanilla JavaScript.
The Debugging Loop: When a bug appears in code you wrote, you know where to look because you remember why you made that choice. When a bug appears in vibecoded logic, you have to prompt the AI to find the bug. If the AI created the bug, it is often just as likely to hallucinate a fix that creates two more bugs.
The Middle Path: AI-Assisted Engineering
I am not saying we should go back to writing every single bracket by hand. That would be foolish. The goal is to move from vibecoding to AI-assisted engineering.
Engineering is the act of making intentional trade-offs. Vibecoding is the act of accepting whatever the AI gives you. To bridge the gap, you need to change your workflow.
First, define the architecture before the prompt. Do not ask the AI to "build a checkout page." Instead, tell it: "I need a React component for a checkout page. It should use a custom hook for state and follow this specific data schema."
Second, treat AI output as a draft, not a final product. Read every line. If you see a function you don't fully understand, ask the AI to explain it, or better yet, rewrite it yourself.
Third, write your own tests. Tests are the only way to ensure that a "vibe shift" in a new prompt doesn't break a feature you finished last week.
The Concrete Takeaway
AI is a tool for acceleration, not a replacement for thinking. If you can't explain exactly how your code works without referencing the prompt you used to generate it, you aren't coding. You are just gambling with your codebase.
Use the AI to handle the boilerplate and the syntax. Use your brain to handle the logic, the security, and the architecture. That is how you build software that lasts longer than a demo video.
Top comments (1)
I like the distinction between vibecoding and AI-assisted engineering. The biggest shift we've seen at IT Path Solutions is treating AI as an accelerator, not the decision-maker. It's excellent for generating a first draft, but architecture, state management, security, and edge cases still need deliberate engineering. The teams getting the best results aren't prompting more they're reviewing more, validating more, and using AI to speed up implementation instead of replacing design thinking. That's what makes the difference between an impressive demo and software that's still maintainable six months later.