Best Practices¶

Guidelines for effective and responsible vibecoding. These practices will help you get better results while maintaining code quality and security.

Code Quality¶

Always Review Generated Code¶

AI can generate code that looks correct but has subtle issues:

Logic errors - The code runs but doesn't do what you expect
Performance issues - Inefficient algorithms or unnecessary operations
Security vulnerabilities - SQL injection, XSS, insecure defaults
Missing edge cases - Happy path works, error cases don't

Never Commit Without Review

Treat AI-generated code like code from a junior developer - it needs review before going to production.

Understand What You Ship¶

If you can't explain how code works, you shouldn't ship it:

Good: "I used this caching decorator because it memoizes expensive
      function calls and invalidates after 5 minutes."

Bad:  "The AI suggested this and it seems to work."

Test Thoroughly¶

AI-generated code needs the same (or more) testing as human-written code:

Unit tests for individual functions
Integration tests for component interactions
Edge case testing (empty inputs, large inputs, invalid data)
Security testing for user-facing code

Security¶

Review for Vulnerabilities¶

Always check generated code for common security issues:

Vulnerability	What to Look For
SQL Injection	String concatenation in queries
XSS	Unescaped user input in HTML
CSRF	Missing token validation
Auth bypass	Missing permission checks
Secrets	Hardcoded API keys or passwords

Be careful what you include in prompts:

Never Include in Prompts

API keys and secrets
Production database credentials
Customer data or PII
Proprietary business logic you don't want shared
Access tokens

When pasting code for review, remove or redact:

# Before
api_key = "sk-abc123realkey456"
db_url = "postgresql://user:realpassword@prod.server.com/db"

# After
api_key = "sk-REDACTED"  # Replace with your API key
db_url = "postgresql://user:password@localhost/db"

Efficiency¶

Break Down Large Tasks¶

Instead of asking for an entire application, break it down:

Step 1: "Help me design the data model"
Step 2: "Create the database schema"
Step 3: "Build the authentication system"
Step 4: "Add the core API endpoints"
Step 5: "Implement the frontend components"

Provide Relevant Context¶

Give the AI what it needs, but not more:

Good: "I have a FastAPI app using SQLAlchemy. Here's my User model:
      [paste model]. I need to add an endpoint to update user profiles."

Bad:  [Pastes entire codebase of 50 files]

Iterate Quickly¶

Don't try to get everything perfect in one prompt:

Get a working version
Test it
Refine with follow-up prompts
Repeat until satisfied

Learning¶

Ask for Explanations¶

Use AI to learn, not just to generate:

"Can you explain why you used a generator here instead of returning
a list? What are the trade-offs?"

Compare Approaches¶

Learn about different solutions:

"Show me how to implement this using:
1. A class-based approach
2. A functional approach
3. Using a library

What are the pros and cons of each?"

Document Your Learnings¶

Keep notes on patterns that work well:

## Vibecoding Notes

### Effective Patterns
- Using docstrings first helps generate better implementations
- Breaking authentication into steps: validation, verification, token generation
- Asking for error handling separately improves code structure

### Lessons Learned
- Always verify regex patterns with edge cases
- Generated SQL needs review for N+1 queries
- Date handling needs explicit timezone specification

Workflow¶

Version Control Everything¶

Commit frequently so you can revert if needed:

git add .
git commit -m "feat: add user validation (AI-assisted)"

Use Descriptive Commits¶

Indicate when AI assisted with the code:

feat: add email validation

- Validates email format using regex
- Checks for common typo domains
- AI-assisted implementation, reviewed for security

Maintain Your Standards¶

AI should adapt to your codebase, not the other way around:

"Here's our code style guide: [summary]
Please follow these conventions in all code you generate."

Checklist¶

Before shipping AI-generated code:

[ ] I understand how this code works
[ ] I've reviewed for security vulnerabilities
[ ] I've tested with normal inputs
[ ] I've tested with edge cases
[ ] I've tested with invalid inputs
[ ] The code follows project conventions
[ ] The code is properly documented
[ ] I've removed any sensitive data
[ ] The code passes all existing tests
[ ] I can explain this code in a code review