7 Essential Best Practices for Effective Vibe Coding

AI-assisted development (or "vibe coding") can dramatically accelerate your software development process, but without proper discipline, it can quickly lead to chaotic, unmaintainable code. At KAPI, we've refined a set of best practices that ensure AI remains a powerful tool rather than a source of technical debt.

1. Manage Branches Properly

When working with AI coding assistants, it's tempting to iterate rapidly and commit directly to main branches. This is a recipe for disaster.

Key principles:

• Never touch the main branch directly

• Create feature branches for all AI-generated code

• Establish clear naming conventions for branches

• Ensure all generated code goes through proper review before merging

By isolating AI-generated work in feature branches, you protect your production codebase while still allowing rapid iteration with AI tools.

2. Run Automated Linting

AI models are trained on diverse codebases with varying standards. Enforce your team's standards with automated linting.

Implementation:

• Run ESLint on frontend code

• Use Ruff/mypy on Python backend code

• Integrate linting into your CI/CD pipeline

• Configure pre-commit hooks to catch issues early

AI will quickly adapt to your team's style once you provide clear feedback through linting tools.

3. Require AI-Written Unit Tests

One of the most powerful applications of AI coding assistants is generating comprehensive test suites. Make this mandatory.

Best approach:

• Require test coverage for all AI-generated code

• Use `poetry run pytest` for Python and `npm run test` for JavaScript/TypeScript

• Set minimum coverage thresholds (we recommend starting at 80%)

• Include edge cases and failure scenarios

Well-tested code is especially important when using AI assistants, as it verifies the AI's understanding of the requirements.

4. Limit Function Size and Complexity

AI models often generate overly complex functions. Set strict limits to maintain readability.

Guidelines:

• Keep functions under 50 lines

• Limit nesting to 3 levels

• Enforce single responsibility principle

• Split complex functions into smaller, more focused ones

These constraints force both human developers and AI assistants to create more modular, maintainable code.

5. Implement AI Code Reviews

Use AI not just for generation, but for review. This creates a powerful feedback loop.

Process:

• Have an AI review all code before human review

• Look for security vulnerabilities, anti-patterns, and optimization opportunities

• Generate suggestions for improvement

• Document common issues to avoid in future iterations

AI code review can catch issues that humans might miss, especially in large codebases.

6. Enforce Documentation Standards

AI-generated code often lacks proper documentation. Make it mandatory.

Requirements:

• Force comments on every function

• Keep documentation concise and focused on "why" not just "what"

• Generate API documentation automatically

• Document assumptions and constraints

Well-documented code is critical when working with AI assistants, as it clarifies the intent behind the implementation.

7. Create Visual Architecture Diagrams

Visual representations help both humans and AI understand complex systems.

Implementation:

• Require a Mermaid diagram for each PR

• Visualize component interactions

• Update diagrams as architecture evolves

• Reference diagrams in documentation

These diagrams become invaluable for onboarding new team members and for AI assistants to understand the broader context of the codebase.

Comprehensive Documentation Framework

Beyond these seven core practices, we've found that providing AI with detailed documentation upfront significantly improves output quality. We recommend creating:

PRD (Product Requirements Document)

• App overview

• User flows

• Tech stack & APIs

• Core features

• In-scope vs out-of-scope items

App Flow Document

• Description of every page in your app

• How users move from one to another

• Simple language, no bullet points

• Painfully specific details

Tech Stack Documentation

• All packages & dependencies

• Links to API docs (AI can read these!)

• Preferred libraries or tools

• Configuration details

Frontend Guidelines

• Fonts

• Color palette

• Spacing & layout rules

• Preferred UI library or framework

• Icon set

Backend Structure Documentation

• DB schema

• Auth logic

• Storage rules

• Edge cases

Implementation Plan

• 50+ specific steps you'd take if coding manually

• Sequenced in logical order

• Dependencies between components

The Payoff

By following these best practices, you'll experience:

1. Higher code quality - AI-generated code that meets or exceeds human-written standards

2. Faster iteration - Clear guidelines that eliminate back-and-forth clarification

3. Better maintainability - Systems that remain understandable and extensible

4. Reduced technical debt - Less need for massive refactoring later

5. Smoother collaboration - Clear standards for both human and AI contributors

The key insight is that AI coding assistants aren't a replacement for software engineering discipline—they make it more important than ever. By establishing these guardrails, you harness AI's speed and creativity while maintaining the quality and maintainability your projects need for long-term success.