What is Vibecoding?
1.1: The One-Sentence Explanation
Vibecoding is using AI as a pair programmer to build software faster, while you stay in the driver's seat for every important decision.
You provide the vision, constraints, and context. AI provides the implementation speed. Together, you ship 10x faster than either could alone.
1.2: How This Differs from Traditional Coding
| Traditional Coding | Vibecoding | |
|---|---|---|
| Starting point | Jump into code | Plan first: idea, research, requirements |
| Writing code | Write every line yourself | Describe what you want, review what AI produces |
| Debugging | Read stack traces for hours | Paste the error log, get a fix in seconds |
| Getting stuck | Google for 45 minutes | Ask AI to research docs and explain |
| Architecture | You decide (slowly) | You decide (quickly, with AI helping explore options) |
| Testing | Often skipped | AI writes tests alongside implementation |
AI handles roughly 80% of the work: boilerplate, patterns, translations, repetitive code. You handle the 20% that actually matters: architecture, security, business logic, and context.
1.3: The Genie Mindset
Think of AI as a genie. It will build anything you ask -- but only if you ask correctly.
This means:
- Precise wishes get precise results. Vague prompts get generic code.
- You are the architect. AI is the builder following your blueprints.
- Context is everything. The more AI knows about your project, the better it performs.
A bad wish: "Add authentication."
A good wish: "Add email/password auth using NextAuth.js. Use the existing User model in schema.prisma. Follow the pattern in AuthService.ts. Rate limit to 5 attempts per minute."
1.4: What AI is Great At
Let AI handle these -- it's faster and usually correct:
- Boilerplate code -- repetitive, pattern-based stuff
- Type definitions -- mechanical translation
- Unit tests -- pattern matching on existing code
- Refactoring -- systematic transformations
- Documentation -- summarizing code intent
- Error messages -- parsing and explaining stack traces
- Regex, SQL, CSS -- tedious but deterministic
1.5: What You Must Control
Never fully delegate these -- they require judgment AI doesn't have:
- Architecture decisions -- long-term implications
- Security-critical code -- too important to trust blindly
- Business logic -- domain knowledge required
- Performance optimization -- requires measurement, not guessing
- Database schema design -- needs understanding of access patterns
- Code review -- always read what AI generates
High trust (let AI do it): boilerplate, tests, types, docs. Low trust (you do it): architecture, security, business logic, deployment. Everything else: collaborate.
1.6: When to Type Instead
Sometimes writing code manually is faster than explaining it to AI:
- Tiny changes -- rename a variable, fix a typo
- Domain-specific logic -- only you understand the business rules
- Flow state -- don't break your rhythm for a one-liner
The goal isn't to use AI for everything. The goal is to use it for the right things so you can focus your energy on what matters: building great products.