Common Vibe Coding Mistakes (and How to Avoid Them)

Mistake 1: Blind Acceptance

The problem: Accepting every AI suggestion without review. This creates code that works initially but contains hidden bugs, security vulnerabilities, and maintenance debt that compounds over time.

The fix: Treat AI output like a junior developer's pull request. Review every change, understand the logic, and test edge cases. Acceptance rate should be 30-40%, not 90%+.

Mistake 2: Context Starvation

The problem: Providing vague, context-free prompts and being surprised by generic output. "Build a login page" produces a generic form. "Build a login page matching our existing design system, using our auth service API, with OAuth via Google" produces something useful.

The fix: Always include: tech stack, existing patterns to follow, specific requirements, and constraints. Reference actual files in your project.

Mistake 3: Mega-Prompts

The problem: Asking AI to build an entire feature in one prompt. The output is superficial — each component gets minimal attention because the model is spreading its reasoning across too many concerns.

The fix: Break tasks into focused steps. Plan → Types → Implementation → Tests → Integration. Each step gets the model's full attention.

Mistake 4: Ignoring Security

The problem: AI generates code that works but is insecure. Hardcoded secrets, SQL injection vulnerabilities, missing input validation, and insecure defaults are common in AI-generated code.

The fix: Explicit security requirements in every prompt. Run SAST tools. Never deploy auth or payment code without manual security review.

Mistake 5: Skipping Tests

The problem: AI-generated code feels "done" because it compiles and runs. Without tests, bugs hide until production.

The fix: Generate tests alongside or before implementation. Use the test-driven vibe coding pattern: write tests first, then ask AI to implement code that passes them.