Vibe Coding Best Practices: Advanced Techniques for AI-Assisted Development

What You'll Learn

This guide covers advanced vibe coding techniques:

• Effective prompting strategies
• Code validation and security
• Team collaboration patterns
• When NOT to use vibe coding
• Measuring productivity gains

Prerequisites

• Completed the Vibe Coding Introduction
• Completed the Vibe Coding Setup
• Basic experience with AI-assisted development

Best Practice 1: Master the Art of Prompting

Be Specific, Not Vague

❌ Bad prompt:

Make a user form

✅ Good prompt:

Create a React user registration form with:
- Email field (validate format)
- Password field (min 8 chars, show/hide toggle)
- Confirm password (must match)
- Submit button (disabled until valid)
- Loading state during submission
- Error display for API failures
Use react-hook-form and Zod for validation.
Follow existing patterns in src/components/forms/

Provide Context

Always give the AI relevant context:

I'm working on an e-commerce checkout flow.
The project uses Next.js 14 App Router with server actions.
We have an existing CartContext in src/context/cart.tsx.
Payment processing is handled by Stripe.

Create a checkout summary component that:
- Displays cart items from CartContext
- Shows subtotal, tax, and total
- Has a "Proceed to Payment" button
- Matches the design in /designs/checkout.figma

Use Examples

Show the AI what you want:

Create an API endpoint for user preferences.
Follow this existing pattern from src/app/api/profile/route.ts:

export async function GET(req: Request) {
  const session = await getSession();
  if (!session) return unauthorized();
  // ... handler logic
}

The new endpoint should handle GET and PATCH for user preferences.

Best Practice 2: Validate AI-Generated Code

Always Review Before Committing

Create a mental checklist:

• Does it match the requirements?
• Are there obvious bugs?
• Does it follow project conventions?
• Are there security issues?
• Is error handling adequate?

Run Tests

# Run existing tests
npm test
 
# Ask AI to generate tests
"Write unit tests for the component you just created.
Cover: normal flow, edge cases, error states."

Use Static Analysis

# TypeScript catches many issues
npm run typecheck
 
# Linting catches style and common mistakes
npm run lint
 
# Consider security-focused linters
npm audit

Best Practice 3: Security First

Never Trust AI with Secrets

❌ Never do this:

Here's my API key: sk-xxxxx
Create a function to call the OpenAI API.

✅ Do this instead:

Create a function to call the OpenAI API.
Assume the API key is available via process.env.OPENAI_API_KEY.
Never log or expose the key.

Watch for Common Vulnerabilities

Ask AI to check for:

• SQL injection (use parameterized queries)
• XSS (sanitize user input)
• CSRF (use tokens)
• Authentication bypasses
• Sensitive data exposure

Review this code for security vulnerabilities:
[paste code]
Focus on OWASP Top 10 issues.

Sanitize AI-Generated SQL

# Bad - AI might generate vulnerable code
"Create a query to find users by email"

# Better - specify security requirements
"Create a parameterized query to find users by email.
Use prepared statements to prevent SQL injection.
Follow existing patterns in src/db/queries.ts"

Best Practice 4: Iterate Incrementally

Start Small, Expand

Instead of asking for a complete feature:

1. Foundation first:

   Create the basic component structure for a dashboard
   

2. Add functionality:

   Add a data fetching hook to load dashboard metrics
   

3. Handle edge cases:

   Add loading and error states to the dashboard
   

4. Polish:

   Add animations and improve accessibility
   

Use Follow-up Prompts

[After initial code]
"Good start. Now:
1. Add TypeScript types for all props
2. Extract the API call to a custom hook
3. Add error boundary handling"

Best Practice 5: Document as You Go

Keep Prompts as Documentation

Save important prompts in your project:

<!-- docs/ai-prompts/checkout-flow.md -->
# Checkout Flow Implementation
 
## Initial Prompt
[paste the prompt you used]
 
## Follow-up Refinements
[paste follow-up prompts]
 
## Key Decisions
- Used server actions for payment processing
- Chose Stripe over PayPal for better API

Let AI Generate Documentation

Generate JSDoc comments for all public functions in this file.
Include:
- @param descriptions
- @returns description
- @throws for possible errors
- @example usage

Best Practice 6: Team Collaboration

Standardize Prompting

Create team prompt templates:

<!-- .github/PROMPT_TEMPLATE.md -->
## Context
[Describe the project/feature context]
 
## Requirements
[List specific requirements]
 
## Constraints
- Must follow [coding standard]
- Must use [specific libraries]
- Must match [design/patterns]
 
## Examples
[Provide relevant code examples]

Review AI-Generated PRs Carefully

Flag AI-generated code in PRs:

## PR Description
 
🤖 **AI-Assisted:** This PR was created with AI assistance.
 
**Human review focused on:**
- [ ] Security implications
- [ ] Performance considerations
- [ ] Edge case handling
- [ ] Test coverage

Best Practice 7: Know When NOT to Use Vibe Coding

Avoid Vibe Coding For:

• Security-critical code: Authentication, encryption, access control
• Complex algorithms: Custom ML models, specialized computations
• Legacy system deep changes: Requires deep contextual understanding
• Compliance-sensitive: Healthcare, finance regulations

Use Vibe Coding For:

• Boilerplate: Forms, CRUD, standard patterns
• Prototypes: Quick proof-of-concepts
• Refactoring: Code transformation, modernization
• Documentation: Comments, READMEs, API docs
• Tests: Unit tests, integration tests

Measuring Success

Track These Metrics

• Time to first working version: Faster with vibe coding
• Bug rate: Should stay same or improve
• Code review time: May decrease with good prompts
• Developer satisfaction: Survey your team

Example Dashboard

Week 1 with Vibe Coding:
- Features delivered: 12 (up from 8)
- Bugs found in review: 3 (same)
- Average PR size: 150 lines (up from 80)
- Time saved: ~15 hours estimated

Summary

Key takeaways:

1. Prompt effectively - Be specific, provide context, show examples
2. Validate rigorously - Review, test, and lint all AI code
3. Security first - Never trust AI with secrets, check for vulnerabilities
4. Iterate incrementally - Build features in small steps
5. Document everything - Save prompts and decisions
6. Collaborate smartly - Standardize team practices
7. Know the limits - Use vibe coding where it excels

Next Steps

• Vibe Coding Hub - All resources
• Get Vibe Coding Assistance - Expert help