Why Vibe-Coded Products Break, And How to Fix It
Vibe coding is one of the fastest ways to build any digital product today. If we are being honest, it has changed how many people approach building products, because what used to take weeks or even months can now be done in a few days, sometimes even in a single day. That speed creates a certain kind of confidence that makes you feel like you are moving in the right direction, as you have finally figured out how to build without being blocked by technical complexity.
Which makes a lot of sense...
But the issue with vibe coding is not at the beginning, because at the beginning, everything always feels like it is working, the flow would make sense, and from the user's view, it looks like you have something solid, something that can actually be shown to people or even taken to market.
And the real problem only starts to show when your product is exposed to pressure, and by pressure, I mean real users, real interactions, real inputs that you cannot manually control.
Because that is when you begin to notice that certain things do not behave the way they should, data does not move properly, some actions fail silently, and the product that once looked complete starts revealing gaps that were never obvious during the build phase.
What most founders do not realise is that this is not because vibe coding is bad, but because of how it is being used.
A lot of people are relying on AI not just to build, but to think, and that is where the disconnect starts, since AI is incredibly good at generating code when you give it instructions, but it does not naturally sit down to plan your product, it does not map out how your system should behave across different scenarios, and it definitely does not take responsibility for how everything connects in the long run.
So what happens is that you end up prompting your way into a product that looks complete on the frontend but lacks any real structure at the backend, and without that structure, things only work in ideal situations, which is why the moment anything slightly different happens, the system starts to break in ways that are difficult to predict or even understand.
That being said...
One of the simplest ways to resolve the issues is by creating a basic architecture.md file, which does not need to be overly detailed, but should clearly explain your stack, your structure, and your rules.
So, for example, you might define that you are using Supabase for your database, Tailwind for your styling, and a specific service for authentication, and then go further to explain how different parts of your product connect and what each part is responsible for.
The same vibe-coding tool can help you create the architecture.md
What this does is that it removes guesswork from the process, because instead of AI making assumptions about your system, you are now giving it context, and context is the difference between code that works temporarily and code that actually holds up over time, since without context AI will always try to fill in the gaps on its own, and those gaps are where most of the problems come from.
Below is an example of an architecture.md
# Architecture Overview
## Stack
Frontend: Next.js
Styling: Tailwind CSS
Backend: Supabase
Authentication: Supabase Auth
Payments: Stripe (optional)
Hosting: Vercel
---
## System Structure
- Frontend handles UI and user interaction
- Backend handles data storage and logic
- API connects frontend to backend
---
## Core Features (Technical)
- User authentication
- Dashboard UI
- Form input system
- Data processing logic
- Result display
---
## System Flow
1. User logs in via Supabase Auth
2. User accesses dashboard
3. User submits data
4. Backend processes request
5. Response is returned
6. UI updates with result
---
## Data Handling
- Store user data in Supabase
- Use secure validation before saving
- Separate user data properly
---
## Rules & Constraints
- Always validate inputs
- Always handle errors
- Keep features modular
- Do not mix unrelated logic
---
## AI Instructions
You are a Senior Full-Stack Engineer.
- Follow the defined stack strictly
- Do not introduce new tools unless necessary
- Write clean and readable code
- Include validation and error handling
- Build one feature at a time
---
## Build Strategy
- Start with authentication
- Then dashboard UI
- Then core feature
- Then testing
---
## Limitations
- MVP only
- Not optimized for scale
- Basic security
---
## Future Improvements
- Performance optimization
- Better UI/UX
- Advanced security
- Analytics integration Another mistake that contributes to this issue is the tendency to build everything at once, where founders ask for a full dashboard or an entire system in a single prompt, expecting the AI to deliver something complete, and while it might return something that looks impressive, what is actually happening behind the scenes is that the AI is cutting corners to fit everything into one response, which leads to missing logic, weak error handling, and inconsistencies across different parts of the product.
A more reliable approach is to build in smaller parts, focusing on one feature at a time, making sure it works properly before moving to the next, because this makes it easier to identify where problems come from and prevents situations where you have to start over just to fix a single issue.
A good approach is to use a conversational AI to list out and make a task breakdown for all that needs to be done, while you pick them one after the other and test extensively before moving to the next phase
Below is a sample tasks.md file
# Task List
## Phase 1: Setup
- [ ] Initialize project
- [ ] Install dependencies
- [ ] Setup Supabase
- [ ] Setup environment variables
---
## Phase 2: Authentication
- [ ] Create sign up page
- [ ] Create login page
- [ ] Connect Supabase Auth
- [ ] Test authentication flow
---
## Phase 3: Dashboard
- [ ] Create dashboard layout
- [ ] Protect routes (auth required)
- [ ] Display user info
---
## Phase 4: Core Feature
- [ ] Build input form
- [ ] Connect form to backend
- [ ] Process user input
- [ ] Display results
---
## Phase 5: Error Handling
- [ ] Handle invalid inputs
- [ ] Show error messages
- [ ] Handle failed requests
---
## Phase 6: Testing
- [ ] Test user flow
- [ ] Fix bugs
- [ ] Improve UX
---
## Phase 7: Launch
- [ ] Deploy to Vercel
- [ ] Test live version
- [ ] Share with first users
---
## Future Tasks
- [ ] Add analytics
- [ ] Improve UI
- [ ] Add more features There is also the issue of trust, because many founders take whatever the AI generates and move forward without questioning it, which might seem efficient in the moment but becomes risky over time, since AI often skips the less obvious but very important parts of a system, things like validation, proper error handling, and edge cases that only show up under real usage.
A simple way to reduce this risk is to introduce a review step, where you take the output from one AI and run it through another, asking questions about what could break, what is missing, and what needs improvement, because having that second layer of review can catch issues that would otherwise go unnoticed until much later.
It is also worth noting that tools like Lovable or v0 provide a level of safety for founders who are not deeply technical, not because they are inherently better, but because they come with built-in structure that reduces the chances of breaking things early, as they handle things like deployment and basic system setup, allowing you to focus more on the product itself.
However, even with these tools, the underlying principle does not change, because if you do not understand the structure of what you are building, you will eventually run into limitations, and those limitations will slow you down in ways that are much harder to fix later.
At the end of the day, you realise that vibe coding is not the problem, and it is not something to avoid, because when used properly, it can significantly speed up how you build, but speed without structure will always lead to fragile systems, and fragile systems do not survive real-world usage.
So the goal is not to stop vibe coding, but to approach it with more intention, by adding a layer of clarity and structure that guides how your product is built, because once you do that, you are no longer just generating code, you are building something that can actually hold up, grow, and evolve.