The 10 Guiding Principles
of Vibe Coding

The original philosophy is about trust: letting AI handle the mechanical aspects of coding while you focus on the creative vision. Accept suggestions freely, iterate quickly, and don't obsess over understanding every line. But this isn't blind faith—you're the architect, the AI is the builder. Know when to go with the flow and when to pull back for review. For prototypes and weekend projects, "Accept All" can be your friend. For production code, selective acceptance becomes essential.

Your prompts are your code now. The AI isn't a mind reader—it's a highly capable collaborator that needs clear direction. Talk to it like you're explaining a project to a skilled colleague: naturally, but with enough specificity to avoid ambiguity. Instead of "Make something cool," try "Build a minimal to-do list app with soft pastel colors, oversized buttons for mobile, and local storage persistence." The sharper your request, the closer the first draft lands to your vision.

The most common mistake? Asking the AI to build everything at once. Complex projects overwhelm even the best models. Break your vision into milestones you can test independently: get the basic structure rendering, add core functionality, refine the user experience, handle edge cases, then polish and deploy. Each step should be something you can run and verify before moving to the next.

Vibe coding thrives on rapid iteration. Rather than crafting one perfect mega-prompt, engage in a conversation. Make a request, test the output, refine with feedback, and repeat. The AI learns your preferences through dialogue. Not all iterations are equal—recognize when changes have architectural impact and plan accordingly. Start fresh chats for new features to avoid bloating the AI's context window.

Just because AI wrote it doesn't mean it works. Run the code after every meaningful change. Testing isn't just validation—it's your primary feedback mechanism. When things break, this is where you step in: know where to find helpful information (error messages, console logs, stack traces), and understand what the AI is trying to achieve. This feedback loop is where real learning happens.

Constraints are as valuable as instructions. Telling the AI what to avoid prevents it from going off-track or breaking things that already work. Explicitly protect critical data: business logic, formulas, core text, data structures, and integration points. For major changes like re-architecting, always run full regression testing—the AI doesn't remember why certain code exists.

If you're using specific frameworks, coding conventions, or design patterns, tell the AI upfront. Better yet, embed these preferences in system rules or project documentation. Include tech stack preferences, code organization patterns, naming conventions, and security requirements. The more context the AI has about your standards, the less time you spend correcting deviations.

This is non-negotiable. AI-generated code can contain vulnerabilities, especially when you're not reviewing every line. Never commit secrets or API keys, review authentication logic manually, run security scans on generated code, and be especially cautious with code handling user input. The AI can help write secure code—but only if you explicitly ask for it and verify the results.

The most sustainable approach combines vibe coding speed with continuous learning. After the AI generates code that works, take time to understand how it works. Ask the AI to explain files in simple terms, add comments, or identify design patterns. Understanding the architecture—even if you didn't write it line by line—makes you a better collaborator and debugger.

Vibe coding is a powerful tool, but it's not the only tool. Security-critical systems, performance-sensitive algorithms, and complex debugging sessions may require you to roll up your sleeves and engage directly with the code. The hybrid approach wins: use vibe coding for rapid prototyping and boilerplate, then apply traditional engineering rigor for critical components.