The Hidden Dangers of Vibe Coding at Scale
When AI helps you move fast—until it quietly breaks what already works.
What People Mean by “Vibe Coding”
“Vibe coding” is the practice of building software by giving high‑level, intent‑based prompts to an LLM and letting it generate or rewrite large portions of the codebase.
Examples:
- “Add Stripe payments.”
- “Refactor this to support subscriptions.”
- “I want a new payment system integrated.”
At small scale, this feels magical. You move fast, features appear instantly, and the app seems to work. The problem begins when your product grows.
The Scaling Problem Nobody Talks About
As your app matures, it accumulates invisible complexity:
- Multiple payment gateways (Stripe, PayPal, local wallets)
- Legacy users with grandfathered rules
- One‑off promos (e.g., Black Friday signups)
- Regulatory or accounting edge cases
- “Temporary” hacks that became permanent
Humans forget these details too—but humans usually don’t rewrite the entire file when asked for a new feature.
LLMs often do.
How High‑Level Prompts Break Existing Logic
Consider this real‑world scenario:
- You already support 2–3 payment gateways
- Black Friday users:
- Locked‑in pricing
- Different renewal rules
- Special cancellation behavior
Now you prompt:
“I want a new payment system integrated.”
From the model’s perspective:
- The prompt is present‑focused
- The most recent instruction has the highest priority
- Anything not clearly represented in the visible context is treated as expendable
The result?
- Old conditional logic is simplified away
- Edge cases are overwritten by a “cleaner” abstraction
- Rare user segments silently lose guarantees you promised them
Nothing crashes. No tests fail.
But production behavior changes.
Context Windows Are a Hard Technical Limit
This isn’t just “AI being careless.” It’s architectural.
LLMs have a finite context window.
As your codebase grows:
- Not all files fit into context
- Not all historical decisions are visible
- Comments explaining why something exists are often omitted
When the model rewrites code:
- It optimizes for internal coherence, not historical correctness
- It prefers consistency over exceptions
From the model’s point of view, deleting them is improving the code.
Why This Is Especially Dangerous in Payments
Payments are a perfect storm:
- Money + trust
- Legal and accounting implications
- Users notice changes immediately
- Rollbacks are messy or impossible
Breaking a UI feature is annoying. Breaking payment logic is existential.
The Illusion of “It Still Works”
One of the most dangerous aspects of vibe coding is that nothing appears broken.
The app builds. The checkout flows. The demo works.
But correctness isn’t binary.
The app works—just not the way it used to.
How to Use LLMs Without Shooting Yourself
Vibe coding isn’t evil—but it needs guardrails.
1. Narrow the Rewrite Surface
Avoid prompts that imply global change.
Bad:
“Refactor the payment system.”
Better:
“Add a new gateway without modifying existing pricing logic or legacy user rules.”
2. Encode Edge Cases as Tests, Not Comments
LLMs respect failing tests more than comments.
If Black Friday users matter, lock their behavior in tests.
3. Document Business Invariants Explicitly
Create documents like:
PAYMENTS_INVARIANTS.md
Feed them into relevant prompts.
4. Treat AI as a Junior Engineer, Not an Architect
A junior dev can implement scoped changes. They should not redefine system boundaries.
Your LLM is no different.
Closing Thought
Your production system is not just code.
It is:
- A history of decisions
- A record of promises
- A map of exceptions that exist for a reason
If you let high‑level prompts rewrite that history unchecked, you’re not vibe coding.
You’re vibe gambling.
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