The "It Works But I Don't Know Why" Problem
This is the biggest gotcha in vibe coding. The AI generates code, it runs, it does what you wanted — but you don't understand how it works. This creates problems:
- When it breaks, you can't debug it yourself
- When you need to modify it, you don't know what's safe to change
- When it has a subtle bug, you won't notice until it causes real damage
- When someone asks how it works, you can't explain it
Mitigation: Ask the AI to explain the code it writes. Use prompts like "Explain this code to me like I'm a beginner" after each generation. You don't need to become an expert, but you should understand the general flow.
Security Vulnerabilities
AI models frequently generate code with security issues:
- SQL injection: AI may not parameterize database queries by default
- XSS (Cross-Site Scripting): Generated HTML/JS may not sanitize user input
- Exposed secrets: API keys sometimes get hardcoded instead of using environment variables
- Weak authentication: AI might implement auth patterns that look correct but have flaws
- Missing input validation: Forms may accept anything without checking
Mitigation: Always explicitly ask for security best practices: "Make sure all database queries use parameterized statements," "Store API keys in environment variables," "Sanitize all user input." Better yet, include security requirements in your kickoff prompt.
Scalability Issues
Vibe-coded projects often work great for small-scale use but fall apart under load:
- Inefficient database queries that work fine with 100 rows but crash with 100,000
- No caching, connection pooling, or optimization
- Everything stored in memory instead of proper databases
- No error handling for edge cases or concurrent users
Mitigation: If your project needs to scale beyond personal use, explicitly prompt for production-ready patterns: "Use connection pooling," "Add pagination," "Implement proper error handling and retries."
The Degradation Loop
This happens in long conversations: you keep iterating, the AI starts breaking things it already fixed, and quality spirals downward.
- The context window fills up and the AI "forgets" earlier code
- Fixes for one thing break another thing
- You end up with a tangled mess that neither you nor the AI can sort out
Mitigation: Save working versions frequently. If a conversation goes past 15-20 exchanges without progress, start fresh. Paste your current working code into a new conversation with clear context about what you want to change.
Hallucinated APIs and Libraries
AI models sometimes confidently use APIs that don't exist, functions with wrong signatures, or deprecated library features:
- Inventing function names that sound right but aren't real
- Using old versions of library APIs that have changed
- Mixing up similar libraries (confusing React Router v5 syntax with v6)
- Generating npm package names that don't exist
Mitigation: When the AI uses a library you haven't used before, verify it exists. Tell the AI which version you're using: "I'm using React Router v6" or "I'm on Next.js 14 with the App Router."
Testing Blindspot
Vibe-coded projects almost never include proper tests unless you explicitly ask for them. This means:
- No automated way to know if changes break existing features
- Refactoring becomes scary because you can't verify correctness
- Edge cases go completely unhandled
Mitigation: After getting core features working, add a prompt: "Write tests for the [feature] using [testing framework]. Cover the main happy path and these edge cases: [list them]."
When NOT to Vibe Code
Financial / Medical Software
Anything where bugs can cause real-world harm. Payment processing, medical records, safety-critical systems need rigorous engineering, not vibes.
High-Security Applications
Authentication systems, encryption, access control — get these wrong and real people get hurt. Don't trust AI-generated security code without expert review.
Performance-Critical Code
High-frequency trading, real-time game engines, kernel code — AI generates "correct" code that may be orders of magnitude too slow for these use cases.
Large Team Codebases
AI-generated code in a 500K-line codebase maintained by 20 developers needs to follow team conventions, pass code review, and integrate with existing patterns. Harder to vibe.
The Honest Assessment
Vibe coding is incredible for prototypes, personal projects, MVPs, internal tools, and learning. It dramatically lowers the barrier to building software. But it's not a replacement for understanding what your code does, especially as projects grow beyond hobby scale. The sweet spot is using vibe coding to build fast while gradually learning enough to understand what the AI generates.