Beyond the Hype: Risks and Best Practices in Vibe Coding

1. Maintainability & Code Quality

AI-generated code might work initially, but be hard to maintain in the long run. The code could be convoluted or non-idiomatic, since the AI optimises for "getting a result" rather than clean architecture. As Simon Willison put it, "Vibe coding your way to a production codebase is clearly risky" because real-world development requires readable, well-structured code that others (and future you) can understand.

2. Debugging Challenges

When you didn't write the code yourself, debugging issues is much harder. AI-generated code may be syntactically correct but lack clear structure or comments, so tracking down a bug feels like deciphering a stranger's code. Moreover, LLMs sometimes write code in roundabout ways that a human might not, making the logic less intuitive.

3. Security Vulnerabilities

AI coding tools don't inherently understand secure coding practices – they'll happily churn out whatever works according to training data, even if it's rife with security flaws. This is one of the most critical limitations of vibe coding. For example, an AI might generate a database query by directly concatenating user input, which works on the surface but opens a glaring SQL injection hole.

A study by Georgetown's CSET found that 30–50% of AI-generated code contained security issues like SQL injection or XSS vulnerabilities.

4. Limited Scope & Complexity

Today's generative models excel at producing code for self-contained tasks or well-trodden patterns. But they struggle with truly novel or large-scale architectures. Vibe coding "can handle basic standard frameworks, but for novel or complex technical requirements, it becomes challenging."

5. "Black Box" Effect

Perhaps the defining trait of true vibe coding is accepting code without fully understanding it. This is inherently risky. If you deploy code that you don't grok, you might also be deploying hidden bugs or logic errors. Kevin Roose's experiment with vibe-coded apps is telling: one of his AI-generated apps unexpectedly fabricated fake reviews on a demo e-commerce site – a behaviour he didn't ask for.

1. Keep a Human in the Loop – Always

Think of the AI as a junior developer intern: it can write lots of code quickly, but you must review and supervise it. Never assume the AI's code is correct or optimal just because it runs. Do code reviews on AI-generated snippets as you would on a human's code.

2. Iterate with Clear, Small Prompts

Guide the AI in bite-sized tasks rather than one giant prompt. Vibe coding works best when you tackle one feature or component at a time. For example, instead of saying "Build me a full e-commerce app," you might start with "Generate a React Native UI for a product list with image, title, and price."

3. Use Checkpoints and Version Control

Because AI code changes can be unpredictable, adopt a habit of frequent commits or save points. After the AI generates a chunk of code and you've refined it to a working state, commit that before moving on. Git is your friend here: treat each prompt/session like a separate branch or commit until you trust the changes.

4. Augment AI with Automated Testing & Scans

Don't just eyeball the AI's code – let tools help you verify it. Write unit tests for critical logic to ensure the AI didn't miss edge cases. If you have a security scanner or linter, run it on the AI-generated code. Treat the AI's output as if you outsourced the code to an unknown developer on the internet – never deploy without scanning and testing it.

5. Stay Security-Conscious

When prompting, it can help to explicitly mention security or reliability requirements. For instance, instead of just asking for a file upload handler, say "secure file upload code that validates file type and size to prevent abuse." Still, never assume the AI covered all bases – do a manual security review.

6. Know When Not to Vibe Code

Perhaps the most important best practice is strategic: decide which parts of your project are suitable for vibe coding. Leverage AI for what it's good at – boilerplate, UI layout, simple CRUD logic – but be cautious on core algorithms, complex state management, or anything where a mistake is catastrophic.

7. Continue Learning from the AI

Rather than treating vibe coding as "AI does all the work," use it as a learning opportunity. When the AI produces code, take time to read through it and understand its approach. You might discover new libraries or techniques this way. Treat vibe coding as pair programming with an AI: you're collaborating, not just delegating.