AI Interview Series 15: What are the Common Pitfalls of Vibe Coding?

Although the "feel/atmosphere-driven" mode of Vibe Coding is great for rapid prototyping and creative exploration, if not controlled, it is easy to fall into several typical pitfalls. The following summarizes from five dimensions: code quality, maintainability, security, requirement evolution, and team collaboration.

1. Code Quality Pitfalls

Because Vibe Coding relies on conversational iteration, each time the user submits a vague modification request (such as "make this button more futuristic"), the AI tends to add new code rather than refactor the original logic. It doesn't know which old code is invalid and dares not delete it, leading to accumulation of redundant and dead code. Additionally, the AI has no unified "code style memory" — each generation may follow different naming conventions (depending on the randomness of training samples), and since users rarely give clear specification constraints, the final code becomes messy and unpredictable. Summary:

1. Redundancy and Dead Code: After multiple patches, the AI leaves behind old implementations, commented-out code blocks, and unused imports because deletion is risky and it chooses to keep them.
2. Inconsistent Naming and Style: The AI randomly extracts styles from training data across different sessions; without enforced standards, camelCase, underscores, and spaces get mixed.
3. Hidden Logic Errors: The AI tends to generate code that works for "common paths" but often ignores edge cases (null values, boundary conditions, concurrency) because such examples are scarce in training data.

2. Maintainability Pitfalls

Vibe Coding iterations are extremely fast; both user and AI focus on "whether the current feature works," leaving almost no time for documentation, comments, or refactoring. The AI lacks long-term memory and won't proactively add docstrings to functions or consider the next developer. Moreover, the AI tends to "solve the immediate need" — either over-engineering a generic framework (assuming the user will need it later) or copying and pasting to achieve a quick implementation, leading to messy abstraction levels. Summary:

1. Lack of Documentation and Comments: The AI defaults to "self-explanatory" code, but complex regex or algorithms are hard to understand; the user doesn't ask, so it doesn't write docs.
2. Over-Abstraction or Under-Abstraction: The AI sometimes applies common design patterns (like Factory, Strategy) even for simple problems; other times it directly copies code blocks because it's too lazy to extract a common function.

3. Security Pitfalls

The AI's training data includes a large amount of open-source code, many examples of which contain historical vulnerabilities (e.g., SQL concatenation, hardcoded keys). In Vibe Coding, users rarely actively request "use parameterized queries" or "read keys from environment variables," so the AI uses the most common (and often insecure) patterns. Additionally, the AI has no "threat model" awareness; it doesn't actively check input filtering or least privilege because it only cares about functionality. Summary:

1. Injection Vulnerabilities: The AI defaults to string concatenation for constructing SQL/commands because that's most common in simple tutorials.
2. Hardcoded Sensitive Information: Examples in training often hardcode API keys; the AI mimics this pattern.
3. Excessive Permissions: For convenience, the AI often uses sudo or w+ mode to open files without considering least necessary permissions.

4. Requirement Evolution Pitfalls

Vibe Coding has no clear boundaries. When a user says "add another feature," the AI does its best to satisfy, but it doesn't know what is "out of scope." The AI also has no concept of priority; it might implement three extra features at once, drowning the core functionality. Additionally, each time a new bug is fixed, the AI doesn't review old features, leading to regressions (fixing A breaks B). Summary:

1. Scope Creep: To "satisfy the user," the AI actively adds seemingly related but non-essential features (e.g., a calculator with history).
2. Feature Regression: When fixing a bug, the AI modifies a shared function without understanding the global logic, causing other dependent features to break.

5. Team Collaboration Pitfalls

The conversational process of Vibe Coding is private interaction between an individual and the AI, leaving no transferable specification documents or design decision records. Different team members each talk to the AI and get code in their own style, leading to numerous merge conflicts. Moreover, the AI doesn't automatically generate commit messages or change logs; the rationale for code evolution is lost, forcing later maintainers to guess. Summary:

1. Non-Reproducible Builds: Different people at different times using the same prompt get different implementations (due to sampling randomness).
2. Lack of Change Tracking: No design documents, no commit messages explaining "why this change was made" — the code becomes a black box.