The Complete Guide to Vibe Coding Best Practices

Vibe coding—using AI tools like Cursor, Claude, and v0 to rapidly build applications—has revolutionized how we ship software. But doing it poorly leads to technical debt, security vulnerabilities, and unmaintainable code. This comprehensive guide covers professional-level techniques for vibe coding effectively.

Research Note: Studies show that while AI-generated code is often functionally correct, only ~10.5% of solutions pass security benchmarks. This guide addresses that gap.

Part 1: Understanding Vibe Coding

What Sets Professional Vibe Coding Apart

Professional vibe coding isn't just about generating code faster—it's about:

| Amateur Approach | Professional Approach | |-----------------|----------------------| | Accept AI output as-is | Review, test, and refine every generation | | Vague, one-shot prompts | Structured, iterative prompt chains | | Fix bugs reactively | Prevent bugs through context and constraints | | Skip documentation | Maintain living documentation for AI context | | Trust blindly | Verify with tests, types, and security audits |

The Vibe Coding Stack

Modern vibe coding typically involves:

• Cursor - AI-native code editor with composer and chat modes
• Claude - Anthropic's AI, excellent for complex reasoning
• v0 - Vercel's UI component generator (shadcn/ui + Tailwind)
• GitHub Copilot - Inline code completion
• ChatGPT / GPT-4 - General-purpose coding assistant

Each tool has strengths. Cursor excels at codebase-aware edits. Claude handles complex multi-file refactors. v0 generates polished UI components. Use them in combination.

Part 2: Project Architecture First

Before writing a single prompt, establish your project foundation.

Define Your Tech Stack Constraints

Create a project specification document that AI can reference:

# Project: CoderVibez
## Tech Stack
- Framework: Next.js 14 (App Router)
- Language: TypeScript (strict mode)
- Styling: Tailwind CSS + shadcn/ui
- Database: Supabase (PostgreSQL)
- Auth: Supabase Auth
- Blockchain: Solana (web3.js)

## Architecture Rules
- Use Server Components by default
- Client Components only for interactivity
- All data fetching in server components or API routes
- Zod for all input validation
- Never expose service role keys to client

## Naming Conventions
- Components: PascalCase (UserCard.tsx)
- Utilities: camelCase (formatDate.ts)
- Constants: SCREAMING_SNAKE_CASE
- Database tables: snake_case

Use Cursor Rules Files

Create a .cursorrules file in your project root. This configures AI behavior project-wide:

# .cursorrules

You are an expert Next.js 14 developer using TypeScript and Tailwind CSS.

## Code Style
- Use functional components with hooks
- Prefer const over let, never use var
- Use async/await over .then() chains
- Always handle errors explicitly
- Add JSDoc comments for public functions

## Architecture
- Server Components by default
- 'use client' only when necessary (hooks, events, browser APIs)
- Colocate related files (component + test + types)
- Use @/ path aliases for imports

## Security
- Never hardcode secrets or API keys
- Always validate user input with Zod
- Use parameterized queries, never string concatenation
- Enable RLS on all Supabase tables

## What NOT to do
- Don't use any type - use unknown if truly unknown
- Don't skip error handling
- Don't use deprecated React patterns (componentWillMount, etc.)
- Don't install new dependencies without explicit approval

Establish Data Models Early

Define your entities and relationships before generating code:

// types/database.ts - Define before coding
interface User {
  id: string;
  email: string;
  role: 'vibe_coder' | 'developer' | 'both';
  created_at: string;
}

interface Project {
  id: string;
  owner_id: string; // FK to User
  title: string;
  status: 'open' | 'in_progress' | 'completed';
  bounty_amount: number;
}

interface Bid {
  id: string;
  project_id: string; // FK to Project
  developer_id: string; // FK to User
  amount: number;
  status: 'pending' | 'accepted' | 'rejected';
}

Part 3: Advanced Prompting Techniques

The Context Layering Pattern

Professional prompts have multiple layers of context:

## Layer 1: Project Context
"This is a Next.js 14 App Router project using TypeScript, 
Tailwind CSS, and Supabase for the backend."

## Layer 2: Current State
"The project currently has:
- User authentication working via Supabase
- A /dashboard page that shows user's projects
- Types defined in src/types/database.ts"

## Layer 3: Specific Task
"Create a new component ProjectCard that displays:
- Project title and description
- Bounty amount in SOL
- Status badge (color-coded)
- 'View Details' button"

## Layer 4: Constraints
"Requirements:
- Use existing Card component from @/components/ui/card
- Match styling of existing UserCard component
- Include loading skeleton state
- Add hover animation"

The "Study First" Pattern

Before generating new code, ask AI to study existing code:

"Before creating the new component, study:
1. src/components/ui/card.tsx - the Card component we'll use
2. src/components/dashboard/user-card.tsx - for styling reference
3. src/types/database.ts - for the Project type definition

Then create ProjectCard.tsx that follows the same patterns."

This produces code that matches your existing codebase style.

Prompt Chaining for Complex Features

Break large features into sequential steps:

# Step 1: Data Layer
"Create the Supabase query function to fetch projects 
with their bid counts. Use the existing supabase client pattern."

# Step 2: Type Definitions  
"Based on what the query returns, create/update TypeScript 
types for ProjectWithBids."

# Step 3: Server Component
"Create a Server Component that fetches projects using 
the function from step 1."

# Step 4: Client Interactivity
"Add a Client Component wrapper that handles the 
filter/sort UI controls."

# Step 5: Error & Loading States
"Add proper error boundaries, loading skeletons, 
and empty state handling."

# Step 6: Tests
"Write tests for the query function and component 
rendering with mock data."

Negative Constraints (What NOT to Do)

Explicitly state what to avoid:

"Create an authentication flow.

DO NOT:
- Use localStorage for tokens (use httpOnly cookies)
- Skip email validation
- Allow weak passwords (require 8+ chars, mixed case, numbers)
- Expose any Supabase service role keys
- Use any deprecated Supabase auth methods
- Add any new npm dependencies"

The Self-Critique Pattern

After generating code, ask for a review:

"Review the code you just generated for:
1. Security vulnerabilities (XSS, injection, CSRF)
2. Performance issues (N+1 queries, unnecessary rerenders)
3. Accessibility gaps (ARIA labels, keyboard navigation)
4. Error handling gaps (what if the API fails?)
5. Edge cases (empty data, very long strings, special characters)

List any issues found and provide fixes."

Part 4: Security-First Vibe Coding

AI-generated code often has security gaps. Build security into your workflow.

The Security Audit Checklist

Run this checklist on every AI-generated feature:

Authentication & Authorization

[ ] All protected routes verify authentication
[ ] Users can only access their own data
[ ] Admin functions check admin role
[ ] Session tokens use httpOnly, secure cookies
[ ] Password requirements enforced
[ ] Rate limiting on auth endpoints

Input Validation

[ ] All user input validated with Zod/Yup
[ ] Validation on both client AND server
[ ] File uploads check type, size, content
[ ] SQL queries use parameterization (no string concat)
[ ] No eval() or dangerouslySetInnerHTML with user data

Data Protection

[ ] RLS enabled on all Supabase tables
[ ] Sensitive data not in client bundle
[ ] API keys in environment variables only
[ ] No secrets in git history
[ ] HTTPS enforced

Secure Code Patterns

Input Validation Pattern

// Always validate with Zod before processing
import { z } from 'zod';

const createProjectSchema = z.object({
  title: z.string().min(1).max(200),
  description: z.string().max(5000),
  bounty: z.number().positive().max(1000),
  // Never allow role/permissions in user input!
});

export async function createProject(input: unknown) {
  // Validate input - throws if invalid
  const validated = createProjectSchema.parse(input);
  
  // Now safe to use
  const { data, error } = await supabase
    .from('projects')
    .insert(validated);
}

API Route Security Pattern

// Complete secure API route
export async function POST(request: Request) {
  try {
    // 1. Authenticate
    const supabase = createClient();
    const { data: { user } } = await supabase.auth.getUser();
    
    if (!user) {
      return Response.json({ error: 'Unauthorized' }, { status: 401 });
    }

    // 2. Parse & Validate Input
    const body = await request.json();
    const validated = createProjectSchema.parse(body);

    // 3. Authorize (business logic check)
    const canCreate = await checkUserCanCreateProject(user.id);
    if (!canCreate) {
      return Response.json({ error: 'Forbidden' }, { status: 403 });
    }

    // 4. Execute
    const { data, error } = await supabase
      .from('projects')
      .insert({ ...validated, owner_id: user.id })
      .select()
      .single();

    if (error) throw error;
    return Response.json(data);

  } catch (error) {
    if (error instanceof z.ZodError) {
      return Response.json({ error: error.errors }, { status: 400 });
    }
    console.error('API error:', error);
    return Response.json({ error: 'Internal error' }, { status: 500 });
  }
}

Part 5: Testing AI-Generated Code

AI doesn't run the code it generates. You must verify everything.

The Testing Pyramid for Vibe Code

        /\
       /  \      E2E Tests (Playwright)
      /----\     - Critical user journeys
     /      \    - Auth flows, payments
    /--------\   
   /          \  Integration Tests
  /------------\ - API routes, DB queries
 /              \ - Component interactions
/----------------\
      Unit Tests (Vitest)
      - Utility functions
      - Validation logic
      - Pure components

Test Patterns for AI Code

Test the Happy Path AND Edge Cases

describe('formatBounty', () => {
  // Happy path
  it('formats positive amounts', () => {
    expect(formatBounty(1.5)).toBe('1.50 SOL');
  });

  // Edge cases AI often misses
  it('handles zero', () => {
    expect(formatBounty(0)).toBe('0.00 SOL');
  });

  it('handles very small amounts', () => {
    expect(formatBounty(0.001)).toBe('0.00 SOL');
  });

  it('handles very large amounts', () => {
    expect(formatBounty(1000000)).toBe('1,000,000.00 SOL');
  });

  it('handles negative (should throw)', () => {
    expect(() => formatBounty(-5)).toThrow();
  });

  it('handles NaN', () => {
    expect(() => formatBounty(NaN)).toThrow();
  });
});

Golden Tests for Regressions

Create snapshot tests that catch unintended changes:

// Capture expected output, fail if it changes
it('renders project card correctly', () => {
  const { container } = render(
    <ProjectCard project={mockProject} />
  );
  expect(container).toMatchSnapshot();
});

Part 6: Performance Optimization

AI code often works but isn't optimized. Here's what to watch for.

React Performance Patterns

Avoid Unnecessary Re-renders

// Bad: Object created every render
<ProjectList filters={{ status: 'open' }} />

// Good: Stable reference
const filters = useMemo(() => ({ status: 'open' }), []);
<ProjectList filters={filters} />

Use Server Components

// Bad: Client component fetching data
'use client';
function ProjectList() {
  const [projects, setProjects] = useState([]);
  useEffect(() => {
    fetch('/api/projects').then(r => r.json()).then(setProjects);
  }, []);
  return <div>{/* render */}</div>;
}

// Good: Server component - no client JS needed
async function ProjectList() {
  const projects = await getProjects(); // Direct DB call
  return <div>{/* render */}</div>;
}

Virtualize Long Lists

// Bad: Render 1000 items
{projects.map(p => <ProjectCard key={p.id} project={p} />)}

// Good: Only render visible items
import { useVirtualizer } from '@tanstack/react-virtual';

function VirtualizedList({ projects }) {
  const virtualizer = useVirtualizer({
    count: projects.length,
    getScrollElement: () => parentRef.current,
    estimateSize: () => 100,
  });
  // Only renders ~10-20 items regardless of list size
}

Database Performance

Avoid N+1 Queries

// Bad: N+1 query
const projects = await supabase.from('projects').select('*');
for (const project of projects) {
  const owner = await supabase
    .from('users')
    .select('*')
    .eq('id', project.owner_id)
    .single();
}

// Good: Single query with join
const { data } = await supabase
  .from('projects')
  .select(`
    *,
    owner:users(id, name, avatar_url)
  `);

Part 7: Version Control & Collaboration

Commit Strategy for AI Code

# Bad: Giant commits
git commit -m "Add project feature"

# Good: Small, atomic commits
git commit -m "Add Project type definition"
git commit -m "Add getProjects query function"
git commit -m "Add ProjectCard component"
git commit -m "Add ProjectList with loading state"
git commit -m "Add tests for project components"

Document AI Decisions

Keep a log of significant AI interactions:

# decisions/2024-01-15-auth-flow.md

## Context
Needed to implement Google OAuth with Supabase.

## AI Tools Used
- Cursor Composer for initial implementation
- Claude for debugging redirect issues

## Key Decisions
- Used @supabase/ssr for server-side auth
- Chose PKCE flow over implicit
- Added custom callback handler for user profile creation

## Issues Encountered
- AI initially used deprecated getSession() - fixed to getUser()
- Missing RLS policies on users table - added manually

## Testing Done
- Manual testing of full OAuth flow
- Tested error cases (cancelled auth, expired tokens)

Part 8: Debugging AI-Generated Code

The Structured Debugging Workflow

When AI code breaks:

1. IDENTIFY: What's the exact error message/behavior?
2. ISOLATE: What's the smallest reproduction case?
3. ANALYZE: Read the code - what SHOULD happen vs what DOES happen?
4. HYPOTHESIZE: What could cause this difference?
5. TEST: Verify hypothesis with console.log, debugger, or tests
6. FIX: Make minimal change to fix the issue
7. VERIFY: Ensure fix works and doesn't break other things
8. DOCUMENT: Note what went wrong for future reference

Common AI Code Bugs

| Bug Type | Example | Fix | |----------|---------|-----| | Stale closure | useEffect with missing deps | Add all deps or use refs | | Type mismatch | Assuming API returns array | Add null checks, validate | | Race condition | Multiple rapid state updates | Use useReducer or debounce | | Memory leak | Missing cleanup in useEffect | Return cleanup function | | Hydration error | Server/client render mismatch | Use dynamic import or useEffect |

Part 9: When to Get Human Help

Vibe coding has limits. Get expert help when:

Security-Critical Features

• Authentication and authorization
• Payment processing
• Handling sensitive user data
• Compliance requirements (GDPR, HIPAA)

Performance-Critical Code

• Database query optimization
• Real-time features at scale
• Complex caching strategies

Complex Business Logic

• Financial calculations
• Multi-step workflows
• Integrations with legacy systems

You've Been Stuck

• Same bug for 2+ hours
• AI keeps generating wrong solutions
• Code works but you don't understand why

Professional Vibe Coder's Checklist

Before shipping any AI-generated feature:

Architecture

• [ ] Follows established project patterns
• [ ] Types are complete and accurate
• [ ] No unnecessary dependencies added

Security

• [ ] Input validation on all user data
• [ ] Authentication checks on protected routes
• [ ] No secrets in code or client bundle
• [ ] RLS policies in place

Quality

• [ ] All TypeScript errors resolved
• [ ] Linter passing with no warnings
• [ ] Tests written for critical paths
• [ ] Error handling complete

Performance

• [ ] No N+1 queries
• [ ] Large lists virtualized
• [ ] Images optimized
• [ ] Bundle size reasonable

UX

• [ ] Loading states implemented
• [ ] Error states handled gracefully
• [ ] Empty states designed
• [ ] Mobile responsive

Accessibility

• [ ] Keyboard navigation works
• [ ] ARIA labels present
• [ ] Color contrast sufficient
• [ ] Screen reader tested

Conclusion

Vibe coding is a powerful skill, but it requires discipline. The best vibe coders:

1. Invest in context - Cursor rules, documentation, type definitions
2. Prompt strategically - Layered context, chained prompts, constraints
3. Verify everything - Tests, security audits, performance checks
4. Commit frequently - Small changes, clear history, easy rollbacks
5. Know their limits - Get help when security or complexity demands it

Master these practices, and you'll ship faster AND with higher quality than most developers—vibe coding or not.

Need help with a specific vibe-coded project? Post it on CoderVibez and get expert assistance from developers who specialize in polishing AI-generated code.