When Vibe Coding Goes Wrong: Essential Verification Strategies

I was helping a friend build a simple inventory tracking system using vibe coding the other day

Everything looked perfect until we tried to run it with real data

The AI had generated code that technically worked but completely missed some basic validation checks

That experience got me thinking about how we verify vibe coded systems before they cause real problems

Verification and observation are the core of system success according to the Ten Principles of Vibe Coding

But what does that actually mean when you’re working with AI-generated code

First let’s talk about testing differently

Traditional testing often focuses on whether code matches specifications

With vibe coding you need to test whether the implementation actually solves the right problem

I’ve seen systems pass all their unit tests while still being completely useless for the actual business need

This happens because the AI might interpret your prompt literally without understanding the context

You need human verification at every major milestone

Another crucial aspect is security scanning

AI models trained on public code repositories can inadvertently reproduce security vulnerabilities they’ve seen before

Automated security tools are no longer optional they’re essential

Run them early and often

Then there’s the question of performance

AI might generate code that works perfectly with small datasets but falls apart when you scale up

Always test with realistic data volumes

Better yet test with slightly larger volumes than you expect to handle

Documentation becomes even more important with vibe coding

You need to keep track of what prompts you used what modifications you made and why

This creates an audit trail that helps when things go wrong

It also helps other team members understand your thinking

Code is capability intentions and interfaces are long-term assets as the principles remind us

Your prompts and specifications matter more than the generated code itself

Spend time refining them

Treat them like the valuable assets they are

One practice I’ve found incredibly useful is the red team approach

Have someone who wasn’t involved in the development try to break the system

They’ll find issues you never considered

This works because they approach the system with fresh eyes and different assumptions

Remember that verification isn’t just about finding bugs

It’s about building confidence in your system

Each successful verification step makes the system more reliable

Each failed verification teaches you something important about how to improve your prompts

The goal isn’t perfection

The goal is continuous improvement through careful verification

What verification strategies have you found most effective in your vibe coding projects