Vibe Coding Best Practices

Project Setup

How you set up your project determines how well AI can help you. A messy setup means messy output. Get this right first.

Start with a rules file

Create a CLAUDE.md or .cursorrules file in your project root. This file tells the AI about your project: what tech stack you use, what coding conventions to follow, and what patterns to avoid. Without it, the AI guesses. With it, the AI follows your standards.

Include your project structure, naming conventions, preferred libraries, and any hard rules. For example: "Always use TypeScript strict mode" or "Never use inline styles." The more specific you are, the less cleanup you do later.

Define architecture before coding

AI is good at writing code. It is bad at making architectural decisions. If you let AI decide your database schema, folder structure, or API patterns, you will regret it when the project grows.

Decide these things yourself (or with your team) before you write a single prompt. Document them in your rules file. Then let AI fill in the implementation details.

Keep context small

AI works best with focused, specific context. If you dump your entire codebase into a prompt, the output quality drops. Instead, point the AI at specific files. Work on one feature at a time. Keep your working set small.

In practice, this means: one feature per conversation, reference files by path, and close old conversations when you start new work. Fresh context produces better results than stale, overloaded context.

Writing Better Prompts

Your prompt quality directly affects your code quality. Vague prompts produce vague code. Here is how to be specific. For more on this topic, see our guide on talking to AI.

Be specific about what you want

Bad prompt: "Add authentication to my app." Good prompt: "Add email/password authentication using NextAuth.js with a PostgreSQL adapter. Create a login page at /login and a signup page at /signup. Use the existing Button component from /components/ui/Button.tsx. Store sessions in the database, not in JWT."

The second prompt takes 30 seconds longer to write. It saves 30 minutes of back-and-forth and rework.

Include constraints and edge cases

Tell the AI what happens when things go wrong. What if the user submits an empty form? What if the API returns a 500? What if the file is larger than 10MB? AI will not think about these cases unless you ask.

A good habit: after writing your prompt, ask yourself "what could go wrong?" Add those scenarios to the prompt before hitting enter.

Reference existing code by file path

Instead of saying "use the same pattern as the other pages," say "follow the pattern in /src/pages/dashboard.tsx." AI tools like Claude Code and Cursor can read your files. Point them to the exact code you want replicated.

This also helps with consistency. When AI can see the existing pattern, it matches it. When it cannot, it invents its own.

Reviewing AI-Generated Code

AI writes code fast. That speed is wasted if you ship bugs. Every line of AI-generated code needs a human review.

Always read what AI wrote

This is the most important practice. Do not accept code you have not read. Even if it looks right at first glance, read it line by line. AI can produce code that looks correct but has subtle logic errors, missing null checks, or wrong assumptions about your data model.

If you do not understand what a piece of code does, do not ship it. Ask the AI to explain it, or rewrite it in a way you understand.

Check for security issues

AI does not think about security by default. Look for: SQL injection (raw string concatenation in queries), missing input validation, exposed API keys, missing authentication checks on protected routes, and overly permissive CORS settings.

Run a quick mental checklist: Can a user send unexpected input? Can they access data they should not see? Can they perform actions they should not perform?

Watch for hallucinated imports and APIs

AI sometimes invents functions, libraries, or API endpoints that do not exist. It writes import { something } from 'library' where something is not a real export. Or it calls an API method that was in an older version but was removed.

Always verify that imported packages exist and that the APIs are current. A quick check of the library documentation takes less time than debugging a phantom import. See our testing guide for more on catching these issues early.

Common Mistakes

We see the same mistakes across projects. Avoiding these will save you hours.

Letting AI make architecture decisions

AI will happily pick a database, choose a framework, or design your folder structure. The problem is that it optimizes for the current prompt, not for your project's future. It does not know your team size, your scaling needs, or your deployment constraints.

Decide architecture yourself. Use AI to implement the architecture you chose.

Not testing each change

When AI writes fast, it is tempting to keep prompting and build three features before testing one. Resist this. Test each change before moving on. A bug in feature one will compound into harder bugs in features two and three.

Small, tested increments beat large, untested batches every time.

Prompts that are too vague

"Make it look better" is not a prompt. "Increase the font size of the hero heading to 3rem, add 2rem padding to the card component, and change the primary button color to #E85D45" is a prompt. The more specific you are, the fewer iterations you need.

Speed Tips

Vibe coding is already fast. These tips make it faster.

Use AI for boilerplate, write complex logic yourself

AI is great at generating repetitive code: CRUD endpoints, form components, database migrations, test scaffolding. Let it handle those. But for business logic with complex conditions, payment flows, or security-sensitive code, write it yourself. You will spend less time debugging than you would spend fixing AI's attempt.

Keep commits small

One feature per commit. One bug fix per commit. When something breaks (and it will), small commits make it easy to find the problem. Large commits with mixed changes are a nightmare to debug and roll back.

Use branch-per-feature

Create a new branch for each feature. This keeps your main branch clean and makes code review simple. If a feature does not work out, you delete the branch. No surgery required on the main codebase.

This also helps when working with AI. Each branch is a fresh context for a specific task. No leftover state from unrelated work.

Putting It All Together

Good vibe coding is not about typing faster or prompting more. It is about clear thinking, good project structure, and careful review. The AI handles the typing. You handle the thinking.

Start with a solid setup. Write specific prompts. Review every line. Test every change. Keep your commits small and your branches focused.

If you want to go deeper, read our guides on talking to AI, setting up your tools, and testing and debugging.