5 Advanced Vibe Coding Tips Every Developer Should Know
AI models are getting smarter, but vibe coding still requires technique. Learn 5 essential tips to maximize productivity with AI coding agents.
AI models are evolving at breakneck speed. GPT-4, Claude Opus, Gemini—each new release pushes the boundaries of what's possible with vibe coding. But here's the hard truth: better AI doesn't automatically mean better results.
You've probably experienced it. You describe what you want, the AI generates code, and... it's not quite right. Maybe it works but misses edge cases. Maybe it's technically correct but architecturally messy. Maybe you hit token limits halfway through and everything falls apart.
The potential of vibe coding is enormous, but unlocking it requires technique. In this guide, you'll learn 5 advanced tips that separate developers who struggle with AI from those who use it to ship 10x faster.
Tip #1: Always Write a Detailed Plan Document First
The biggest mistake in vibe coding is jumping straight to code generation. You have an idea, you describe it to AI, and you start building. This works for trivial tasks, but it breaks down fast for anything complex.
Why planning matters: AI models follow your lead. If you're unclear about requirements, architecture, or edge cases, the AI will be too. Garbage in, garbage out.
The Planning Framework
Before generating any code, create a plan document that covers:
1. Feature Requirements
- What exactly are you building?
- What are the core user stories?
- What's out of scope for this iteration?
2. Technical Architecture
- What's the data model?
- What are the key components/modules?
- How do they interact?
3. Edge Cases & Error Handling
- What could go wrong?
- How should the system handle failures?
- What are the validation rules?
4. Success Criteria
- How do you know it's working correctly?
- What should you test?
- What are the performance requirements?
Example: Bad vs Good Planning
Bad (vague):
Build a user authentication system with email and password.
Good (specific):
## User Authentication System
### Requirements
- Email/password registration and login
- JWT-based session management (7-day expiry)
- Password requirements: min 8 chars, 1 uppercase, 1 number
- Rate limiting: max 5 login attempts per 15 minutes
- Email verification required before first login
### Architecture
- `/api/auth/register` - POST endpoint for registration
- `/api/auth/login` - POST endpoint for login
- `/api/auth/verify` - GET endpoint for email verification
- Middleware: `requireAuth` for protected routes
- Database: users table with email, hashed_password, verified, created_at
### Edge Cases
- Duplicate email registration → 409 error
- Invalid credentials → 401 error with generic message (security)
- Unverified email login → 403 error with resend option
- Expired verification link → Generate new link
### Success Criteria
- All endpoints return correct status codes
- Passwords are hashed with bcrypt (cost factor 10)
- JWTs are signed with environment variable secret
- Rate limiting works correctly
When you give AI a detailed plan, it generates code that's architecturally sound, handles edge cases, and meets your actual requirements.
Pro Tip: Use AI to Help You Plan
You can use AI to draft the plan itself:
I want to build a feature that [high-level description].
Before writing any code, help me create a detailed plan
covering requirements, architecture, edge cases, and
success criteria.
Review and refine the plan, then use it to guide code generation.
Tip #2: Make AI Critique Its Own Work
AI-generated code often has blind spots. It might work for the happy path but miss security vulnerabilities, performance issues, or accessibility problems.
The solution? Make AI review its own code from different perspectives.
The Self-Critique Framework
After AI generates code, don't accept it immediately. Ask it to critique from multiple angles:
1. Security Review
Review the code you just generated for security vulnerabilities.
Check for: SQL injection, XSS, CSRF, exposed secrets,
insecure authentication, and insufficient input validation.
2. Performance Review
Analyze this code for performance issues. Look for:
- Inefficient database queries (N+1 problems)
- Missing indexes
- Unnecessary re-renders (React)
- Memory leaks
- Blocking operations
3. Accessibility Review
Check this UI component for accessibility issues:
- Semantic HTML
- ARIA labels where needed
- Keyboard navigation
- Screen reader compatibility
- Color contrast ratios
4. Code Quality Review
Review for code quality and maintainability:
- Is the code DRY (no duplication)?
- Are functions single-responsibility?
- Is naming clear and consistent?
- Are there adequate comments for complex logic?
- Does it follow project conventions?
Example: Security Critique
Original AI-generated code:
// Login endpoint
app.post("/api/login", async (req, res) => {
const { email, password } = req.body;
const user = await db.query(`SELECT * FROM users WHERE email = '${email}'`);
if (user && user.password === password) {
res.json({ token: generateToken(user.id) });
} else {
res.status(401).json({ error: "Invalid credentials" });
}
});After security critique:
// Login endpoint with security fixes
app.post(
"/api/login",
[
rateLimit({ windowMs: 15 * 60 * 1000, max: 5 }),
body("email").isEmail().normalizeEmail(),
body("password").isLength({ min: 1 }),
],
async (req, res) => {
const errors = validationResult(req);
if (!errors.isEmpty()) {
return res.status(400).json({ errors: errors.array() });
}
const { email, password } = req.body;
// Fixed: Use parameterized query to prevent SQL injection
const user = await db.query("SELECT * FROM users WHERE email = $1", [
email,
]);
// Fixed: Use bcrypt to compare hashed passwords
if (user && (await bcrypt.compare(password, user.password_hash))) {
res.json({ token: generateToken(user.id) });
} else {
// Fixed: Generic error message to prevent email enumeration
res.status(401).json({ error: "Invalid credentials" });
}
}
);The AI caught SQL injection, plaintext password comparison, lack of rate limiting, and input validation issues.
Making It a Habit
After every significant code generation, run through at least 2-3 critique perspectives. This catches issues before they reach production.
Tip #3: Set Up Automated Harnesses
AI can't verify its own code is correct. It can generate tests, but it can't run them. This is where automated harnesses become critical.
A harness is a set of automated checks that AI can trigger to validate its work.
Essential Harnesses
1. Linting (ESLint, Prettier)
Catch syntax errors, style violations, and common mistakes automatically:
# Let AI run this after generating code
npm run lintConfigure strict rules so AI gets immediate feedback when it breaks conventions.
2. Type Checking (TypeScript)
Type errors are the most common AI mistake. Set up strict TypeScript:
// tsconfig.json
{
"compilerOptions": {
"strict": true,
"noUnusedLocals": true,
"noUnusedParameters": true,
"noImplicitReturns": true,
"noFallthroughCasesInSwitch": true
}
}After code generation:
npm run type-check3. Build Process
Ensure the code actually compiles and bundles:
npm run buildThis catches import errors, missing dependencies, and configuration issues.
4. Unit Tests
For critical logic, have AI generate tests, then run them:
npm run testIf tests fail, AI can analyze the error output and fix the code.
The Feedback Loop
Here's the workflow:
- AI generates code
- AI runs harnesses (lint, type-check, build, test)
- If failures occur, AI analyzes error output
- AI fixes issues
- Repeat until all harnesses pass
Example prompt:
After generating the code, please run:
1. npm run lint
2. npm run type-check
3. npm run build
If any fail, analyze the errors and fix the code.
Repeat until all checks pass.
This creates a self-correcting loop that dramatically improves code quality.
Pro Tip: Pre-commit Hooks
Set up Husky to run harnesses automatically before commits. This ensures AI-generated code always meets quality standards:
npm install --save-dev husky
npx husky initAdd to .husky/pre-commit:
npm run lint
npm run type-check
npm run testTip #4: Keep Context Usage Below 80%
Every AI model has a context window—the amount of information it can process at once. Claude Opus has 200K tokens, GPT-4 has 128K, etc.
Here's the problem: AI performance degrades significantly when context usage exceeds 80%.
This phenomenon is called context rot. When the context window fills up:
- AI starts "forgetting" earlier parts of the conversation
- Responses become less accurate
- It contradicts itself or ignores previous instructions
- Code quality drops noticeably
How to Monitor Context Usage
Most AI tools show token usage. For example, Claude Code displays:
Token usage: 160000/200000
That's 80%—you're at the danger zone.
Strategies to Stay Below 80%
1. Use Focused Conversations
Don't cram everything into one mega-thread. Break work into focused sessions:
- One conversation for planning
- Another for implementation
- Separate threads for different features
2. Summarize and Reset
When approaching 80%, ask AI to summarize progress:
We've accomplished a lot. Please summarize:
1. What we've built
2. Current state of the codebase
3. What's left to do
I'll start a fresh conversation with this summary.
Copy the summary and start a new thread.
3. Use File References Instead of Pasting
Instead of pasting entire files into chat:
❌ Here's the entire UserService.ts file [paste 500 lines]
✅ Review the file at src/services/UserService.ts
AI can read files without consuming context for the full content.
4. Be Selective About Context
Only include relevant information. If you're working on a specific component, AI doesn't need the entire application codebase.
Measuring Context Rot
You'll know you're experiencing context rot when:
- AI references code that doesn't exist
- It suggests changes that contradict earlier decisions
- It asks questions you already answered
- Code quality noticeably declines
When this happens, reset the conversation immediately.
Tip #5: Use Token Optimization Tools
Tokens cost money and fill context windows. Smart developers use tools specifically designed to optimize token consumption.
Tool #1: Sub Agents
Sub agents are specialized AI assistants focused on specific tasks. Instead of one giant AI conversation handling everything, delegate to focused agents:
- Design agent: Handles UI/UX decisions
- Backend agent: Focused on API and database logic
- Testing agent: Generates and runs tests
- Documentation agent: Writes docs and comments
Why this saves tokens:
- Each agent only needs context for its domain
- No need to explain the entire project repeatedly
- Specialized agents are more efficient at their tasks
How to set up sub agents:
Many AI platforms support this. In Claude Code, you can create task-specific agents with custom prompts and tools.
Tool #2: Serena MCP
Serena MCP is an AI-powered conversation optimizer. It automatically:
- Compresses conversation history
- Removes redundant context
- Surfaces only relevant information
Installation:
claude mcp add serena-mcpSerena runs in the background, keeping your conversations lean without losing important context.
Tool #3: Monet MCP
When building UIs, describing components in detail consumes massive tokens:
Create a hero section with a gradient background from purple
to indigo, a heading in 48px bold font, a subheading in 18px
regular font, two CTA buttons with rounded corners and hover
effects, a background pattern with subtle dots, responsive
layout that stacks on mobile...
That's 50+ tokens for one component.
Monet MCP solves this:
Find a hero section from Monet that matches my brand style.
AI searches Monet's component gallery, finds the right component, and integrates it—using a fraction of the tokens.
Why Monet MCP is essential for vibe coding:
- Massive token savings: Reference pre-built components instead of describing from scratch
- Higher design quality: Components are professionally designed and production-tested
- Faster iteration: No back-and-forth refining AI-generated UI
- Consistent results: Components work the same way every time
Installation:
For Claude Code:
claude mcp add --transport http monet-mcp \
https://www.monet.design/api/remote/mcp \
--header "Authorization: Bearer your-api-key-here"For other IDEs (Cursor, etc.):
{
"mcpServers": {
"monet-mcp": {
"url": "https://www.monet.design/api/remote/mcp",
"headers": {
"Authorization": "Bearer your-api-key-here"
}
}
}
}Get your API key from the MCP setup page.
Usage example:
I need a pricing section for a SaaS product with three tiers.
Use Monet MCP to find a suitable component.
AI finds the component, integrates it, and you customize it with your content and brand colors—all while using 90% fewer tokens than generating from scratch.
Token Optimization Checklist
- Use sub agents for specialized tasks
- Install Serena MCP for conversation compression
- Set up Monet MCP for UI components
- Reference files instead of pasting content
- Break work into focused conversations
- Monitor token usage and reset before 80%
Putting It All Together
Let's see these tips in action with a real example:
Scenario: Building a user dashboard with authentication, data visualization, and settings.
Step 1: Planning (Tip #1)
Create a detailed plan for a user dashboard covering:
- Authentication flow
- Dashboard layout and components
- Data fetching and state management
- Settings page functionality
Step 2: Set Up Harnesses (Tip #3)
Before we start coding, ensure these commands work:
- npm run lint
- npm run type-check
- npm run build
- npm run test
Step 3: Use Token Optimization (Tip #5)
For the dashboard UI, use Monet MCP to find:
- A hero/header component
- A stats/metrics card component
- A data table component
Step 4: Generate Code with Harness Feedback
Implement the authentication module according to the plan.
After implementation, run all harness checks and fix any
errors until they pass.
Step 5: Self-Critique (Tip #2)
Review the authentication code for:
- Security vulnerabilities
- Performance issues
- Code quality problems
Step 6: Monitor Context (Tip #4)
[Check token usage]
Currently at 75000/200000 tokens (37.5%)
Safe to continue.
By following this workflow, you get:
- Well-architected code (detailed planning)
- Secure and performant implementation (self-critique)
- High code quality (automated harnesses)
- Efficient token usage (optimization tools)
- No context rot issues (monitoring)
Conclusion
AI coding agents have incredible potential, but unlocking it requires technique. The difference between frustration and 10x productivity comes down to five principles:
- Write detailed plans before generating code
- Make AI critique its work from multiple perspectives
- Set up automated harnesses for continuous validation
- Monitor context usage and reset before hitting 80%
- Use optimization tools like sub agents, Serena MCP, and Monet MCP
Master these techniques and you'll find that vibe coding transforms from "sometimes useful" to "fundamentally changes how you build."
Speaking of optimization tools: if you're building any kind of frontend or landing page, Monet MCP is non-negotiable. It simultaneously boosts design quality and saves massive amounts of tokens. Instead of spending 10 minutes describing a component and iterating on AI-generated results, you get production-ready components in seconds.
Ready to level up your vibe coding workflow? Start by browsing the Monet component gallery to see what's available, then set up Monet MCP to access components directly from your AI conversations. Your token budget (and your users) will thank you.
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