Vibe Coding: Intent First, Syntax Second
Vibe coding: a development style where the developer describes what they want in natural language ('build a dashboard that shows user activity over the last 30 days with a chart and a data table') and the AI generates the implementation. The developer: focuses on the intent (what the feature should do) rather than the syntax (how to implement it). The name 'vibe coding': coined to describe the shift from writing code line by line to describing the vibe of what you want and letting AI handle the details.
How it differs from traditional coding: traditional coding: the developer writes every line, makes every decision (which library, which pattern, which variable name), and controls every detail. Vibe coding: the developer describes the goal, the AI makes implementation decisions, and the developer reviews and adjusts the result. The developer's role: shifts from writer to director. They are not typing code — they are directing the AI to produce code that matches their intent.
The spectrum: vibe coding is not all-or-nothing. Pure vibe coding: 'Build a complete authentication system' (the AI handles everything). Guided vibe coding: 'Build an authentication system using NextAuth v5 with GitHub OAuth and email magic links' (the developer specifies key decisions, the AI handles the implementation). Traditional + AI assist: the developer writes the code structure, the AI fills in function bodies. Most developers in 2026: use guided vibe coding — describing intent with enough specificity to get good results without writing every line.
How Vibe Coding Works in Practice
The vibe coding workflow: (1) describe the feature in natural language ('Create a user settings page where users can update their name, email, and avatar. Include form validation and save to the database.'). (2) The AI generates the implementation (the component, the form, the validation schema, the API endpoint, and the database query). (3) Review the output (does it match the intent? Does it follow the project's conventions? Does it handle edge cases?). (4) Adjust as needed ('Add a loading state while saving. Show a success toast after save.'). The iterations: refine the result until it matches the vision.
What vibe coding produces well: CRUD features (forms, lists, detail views — these follow well-established patterns), UI components (layouts, navigation, responsive designs — the AI excels at visual structure), API endpoints (standard REST endpoints with validation and error handling — predictable patterns), and tests (given a function: generate tests that cover the main cases — the AI follows testing patterns effectively). These: account for 60-70% of typical application development.
What vibe coding struggles with: complex business logic (domain-specific calculations, multi-step workflows with intricate rules — the AI does not know your business domain), performance-critical code (algorithms that require optimization for specific data characteristics), security-sensitive code (authentication flows, encryption, access control — the AI may generate patterns with subtle vulnerabilities), and novel problems (algorithms or architectures that have not been widely implemented — the AI has less training data to draw from). AI rule: 'Vibe coding for the 60-70% that follows patterns. Traditional coding for the 30-40% that requires domain expertise, security rigor, or novel solutions.'
First prompt: 'Create a user settings page with name, email, and avatar fields.' The AI: generates the complete page (80% correct). Second prompt: 'Add a loading spinner while saving.' Third: 'Show a toast notification on success.' Fourth: 'Handle the case where the email is already taken.' Each iteration: more specific. The broad vibe: gives the AI the big picture. The specific refinements: address the details. This workflow: faster than specifying everything upfront (which is hard to do completely) and more controlled than accepting the first generation (which misses edge cases).
Why AI Rules Make Vibe Coding Sustainable
Vibe coding without rules — the technical debt accelerator: the developer vibes a feature. The AI generates code using its default patterns. The next developer vibes a similar feature. The AI generates code using slightly different patterns (different context, different prompt phrasing). After 10 vibe-coded features: 10 different coding styles in the same codebase. The technical debt: accelerating because every feature adds a new style. Maintenance: increasingly difficult because nobody can predict which pattern any given file follows.
Vibe coding with rules — sustainable velocity: the developer vibes a feature. The AI reads the rules and generates code following the project's conventions. The next developer vibes a similar feature. The AI reads the same rules and generates code following the same conventions. After 10 vibe-coded features: one consistent coding style. The technical debt: minimal because the rules ensure consistency regardless of which developer vibed which feature. Maintenance: predictable because every file follows the same patterns.
Rules are the guardrails for vibe coding: vibe coding gives the AI more autonomy (the developer describes intent, the AI chooses the implementation). More autonomy: requires stronger guardrails. Rules: are those guardrails. They ensure the AI's autonomous decisions align with the team's conventions, even when the developer's prompt does not specify the convention. Without rules: vibe coding is fast but produces inconsistent, hard-to-maintain code. With rules: vibe coding is fast AND produces consistent, maintainable code. AI rule: 'Rules do not slow down vibe coding. They make it sustainable. The same speed of development — with the consistency that prevents technical debt.'
Feature 1 vibed by Alice: try-catch error handling, default exports, Jest tests. Feature 2 vibed by Bob: Result pattern, named exports, Vitest tests. Feature 3 vibed by Carol: Promise.catch, barrel exports, no tests. Each developer: described their intent. Each developer's AI: chose different patterns (no rules to guide the decisions). The codebase: 3 features, 3 styles. After 10 features: unmaintainable. Rules: ensure all 10 features follow the same patterns regardless of who vibed them.
Vibe Coding Best Practices
Be specific about what, flexible about how: 'Create a user settings page with name, email, and avatar fields. Form validation. Save to database.' (Specific about WHAT the feature does.) The AI: decides HOW (which components, which validation library, which database query). The rules: guide the HOW to follow your conventions. The developer: does not need to specify 'use Zod for validation' if the rules already say it. The rules: handle the implementation decisions. The developer: focuses on the feature requirements.
Review AI output critically: vibe coding does not mean blindly accepting every AI generation. Review: does the output match the described intent? Does it follow the project's patterns? Does it handle edge cases? Is the logic correct? The review: faster than with traditional coding (you are reviewing existing code, not writing it) but equally important. The AI: generates plausible code that may have subtle logical errors. The developer: catches these through review, not through writing.
Iterate with specificity: the first vibe generates 80% of the feature. The remaining 20%: refined through specific follow-up prompts. 'Add a loading spinner while the form is saving.' 'Show a toast notification on success.' 'Handle the case where the email is already taken.' Each iteration: more specific than the initial vibe. The refinement: narrows from broad intent to specific behavior. AI rule: 'Vibe broadly first (the overall feature). Then refine specifically (edge cases, states, error handling). The rules: ensure every iteration follows your conventions.'
CRUD features, UI components, API endpoints, tests: the AI generates these well because they follow established patterns. The AI: has seen millions of examples. Vibe coding: efficient for these tasks. Complex business logic (revenue share calculations with tiered commissions), performance optimization (custom data structures for specific query patterns), and security flows (multi-factor authentication with device trust): require human expertise. The developer: vibes the routine 60-70% and codes the critical 30-40% by hand.
Vibe Coding Quick Reference
Quick reference for vibe coding.
- What: describe intent in natural language, AI generates the implementation. Intent over syntax
- Spectrum: pure vibe (AI decides everything) → guided vibe (developer specifies key decisions) → AI-assisted traditional
- Works well for: CRUD, UI components, API endpoints, tests (60-70% of development)
- Struggles with: complex business logic, performance-critical code, security-sensitive flows, novel problems
- Without rules: vibe coding accelerates technical debt (10 features = 10 coding styles)
- With rules: vibe coding is fast AND consistent (10 features = 1 consistent coding style)
- Best practice: be specific about what, flexible about how. Rules handle the how
- Review: still essential. The AI generates plausible code that may have subtle errors. Always review