AI-generated code is becoming increasingly common in modern development workflows. Tools like GitHub Copilot, ChatGPT, and Devin offer speed and convenience — suggesting functions, writing boilerplate, and even scaffolding full-featured modules. Meanwhile, “vibe coding” — a term capturing the flow-like collaboration between developers and AI — is redefining how we build software.
But here’s the truth: AI-generated code still needs review. And debugging AI-assisted code requires the same attention to detail (if not more) as hand-written code.
As a developer and technical consultant, here’s how I help teams review, debug, and production-proof AI-generated and vibe-coded projects.
Reviewing the Logic Behind the Suggestions #
AI tools can’t fully understand your business rules or long-term goals. That’s where I come in.
I walk through AI-generated code and check:
- Does this logic actually make sense for your use case?
- Are there edge cases that the AI didn’t consider?
- Is this code maintainable and scalable?
Code might run, but if it doesn’t serve your product, it’s not helping.
Identifying Bugs and Anti-Patterns #
AI can introduce:
- Inefficient loops
- Deprecated API usage
- Hardcoded values or insecure patterns
I bring years of backend development and systems experience to recognize when code “looks right” but is quietly brittle. I test for:
Input/output validation #
- Missing type checks
- Lack of null checks or default values
- Unsanitized input
- Unsafe assumptions about structure or presence of fields
API misuse #
- Incorrect HTTP method (e.g., using GET when POST is required)
- Missing Authorization headers
- Skipping proper content type (like application/json)
- Ignoring rate limits, error codes, or retries
- Misunderstanding request/response formats
Runtime behavior under stress #
- Can it handle large datasets without crashing?
- Are there memory leaks from infinite loops or unclosed streams?
- Does the code gracefully handle missing APIs or services?
- Is concurrency handled safely in async, fiber, or thread-based logic?
- Are timeouts, retries, and fail-safes properly implemented?
Integrating Code into Real-World Environments #
AI doesn’t know what your infrastructure looks like — I do.
When I review AI-generated code, I:
- Adapt it for your framework (Drupal, Node.js, Django, etc.)
- Ensure it works within your CI/CD pipeline
- Align it with your deployment strategy (serverless, containers, monoliths)
This turns raw code into something deployable.
Writing Unit Tests for AI Code #
AI often skips testing — I don’t.
For every feature or function generated, I help:
- Write meaningful unit and integration tests
- Mock dependencies properly
- Validate edge cases and error handling
This turns suggestions into stable code.
Debugging Vibe Coding Projects in Real Time #
Vibe coding isn’t just about code completion — it’s a development style where you work in rhythm with AI.
In these sessions, I help developers:
- Stay in flow while still being critical of the output
- Adjust prompts for better AI responses
- Step through and debug unexpected results on the fly
This is where engineering and intuition meet.
Final Thoughts #
AI can assist — but it’s not a replacement for experience, context, and judgment.
If your team is exploring AI tools or building projects with vibe coding techniques, I can help ensure:
- Your code works as expected
- It’s safe to deploy
- It aligns with your long-term vision
Let’s make sure your AI-enhanced development process doesn’t just move fast — but moves in the right direction.
Need help reviewing or debugging AI-generated code?
I offer code audits, hands-on development support, and dev team mentorship. Let’s connect.
