VERSALIST GUIDES

Vibe Coding: AI-Assisted Development

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Table of Contents

Introduction

Coding with AI is powerful but unpredictable. This guide distills what actually works from hands-on experience—practical approaches for using AI assistants like Copilot, Claude, and GPT to build real projects.

Who Is This Guide For?

Developers who want practical advice for AI-assisted coding that translates to real-world projects. Whether you're starting with Copilot or building complex features with Claude, you'll find battle-tested approaches here.

1Planning with AI

The difference between success and a hot mess often comes down to planning.

Start with an Implementation Plan

Have the AI write a detailed implementation plan in markdown before coding. This clarifies requirements and identifies issues early.

Implementation Plan Prompt
I need to build a user authentication system for a React/Node app. Generate a detailed implementation plan that includes: 1. Component breakdown with specific files to create 2. Database schema design 3. API route structure 4. Implementation sequence with dependencies 5. Testing approach

Review Before Coding

Don't accept the first plan. Delete unnecessary complexity, mark features as "v2" if they seem too intricate, and keep the scope tight.

Create a "won't do" list alongside your to-do list. Deciding what you're NOT implementing is just as important as what you are.

Checklist

  • Create implementation plan in markdown
  • Review and remove unnecessary complexity
  • Implement section by section
  • Commit each working section before moving on

Version Control for AI Coding

AI-generated code requires a different approach to version control.

Use Git Religiously

Don't trust the AI tools' revert functionality. Commit more often than usual, with smaller, logical changes.

Clean Start Approach
# Start each feature with a clean slate git checkout -b feature/user-auth main # Make small, logical commits git add src/components/LoginForm.jsx git commit -m "Add login form component with validation" # When AI goes off track git reset --hard HEAD~1 # ...try a different approach

Reset When Stuck

If the AI starts generating increasingly complex solutions to fix its own bugs, use git reset --hard HEAD and start fresh.

AI agents are bad at handling complex merge conflicts. Handle conflict resolution manually—the AI will often duplicate code with subtle bugs.

Checklist

  • Start each feature with a clean Git branch
  • Commit small, logical changes frequently
  • Use git reset when AI goes down a rabbit hole
  • Handle merge conflicts manually

3Testing Strategies

AI is great at writing code but terrible at imagining how users break it.

Focus on Integration Tests

AI writes decent unit tests, but they rarely catch real issues. Focus on end-to-end integration tests that verify user journeys.

Integration Test Example
describe('User Authentication Flow', () => { test('User can register, verify, and log in', async () => { const registerRes = await registerUser({ email: 'test@example.com', password: 'SecurePassword123!' }); expect(registerRes.success).toBe(true); const verifyRes = await verifyEmail(registerRes.verificationToken); expect(verifyRes.success).toBe(true); const loginRes = await loginUser({ email: 'test@example.com', password: 'SecurePassword123!' }); expect(loginRes.token).toBeDefined(); }); });

Catch Regressions

AI tools frequently make unnecessary changes to unrelated logic when refactoring. Write tests that catch these specific issues.

Consider writing tests first, then having AI implement features that pass those tests. This provides clear boundaries for what the AI should build.

Checklist

  • Prioritize integration tests over unit tests
  • Test user journeys by simulating real usage
  • Write tests that catch unrelated logic changes
  • Include edge cases from real-world usage

4Bug Fixing

When bugs appear in AI-generated code:

Leverage Error Messages

Copy-paste the exact error message with stack trace. This is often enough for AI to identify and fix issues.

Analyze Before Coding

Ask the AI to consider multiple possible causes before jumping to a solution. This prevents fixing symptoms rather than root causes.

Try Different Models

If one AI model gets stuck, try a different one. Claude may solve problems GPT struggles with, and vice versa.

Be wary of AI's tendency to implement increasingly complex solutions when simple fixes don't work. If it suggests rewriting entire modules to fix a small bug, step back and reassess.

Checklist

  • Provide complete error messages with stack traces
  • Ask for analysis of multiple possible causes
  • Reset after failed fix attempts
  • Try different AI models when stuck

5Optimizing AI Tools

Create Instruction Files

Store detailed instructions in project files (cursor.rules, .claude.md) for consistent AI behavior.

Example .claude.md file
# Project Guidelines ## Code Style - Use TypeScript for all new files - React functional components with hooks - Named exports over default exports - Tailwind CSS for styling ## Naming Conventions - Components: PascalCase - Hooks: camelCase with 'use' prefix - Utilities: camelCase ## Architecture - Components in /components - Hooks in /hooks - Keep components under 150 lines

Keep Local Documentation

Download API documentation to your project folder. AI tools often hallucinate API details or reference outdated versions.

Use Tool Specialization

Cursor is generally faster for frontend work. Claude tends to think longer but produces more robust solutions for complex backend problems.

Create a project-specific prompt library with proven prompts for common tasks. Refine these over time as you learn what works.

6Building Complex Features

Create Standalone Prototypes

Build complex features in a standalone codebase first, where AI can focus without existing system complexity.

Prototyping Approach
# Create a minimal sandbox project npx create-react-app ai-prototype # Install only needed dependencies cd ai-prototype && npm install chart.js react-chartjs-2 # Have AI build the feature in isolation # Once working, integrate into main project

Use Reference Implementations

Point the AI to working examples of similar functionality. Once you show a reference, it often produces working solutions quickly.

Define Clear Boundaries

Maintain consistent external APIs while allowing internal changes. This helps AI understand what it can and cannot modify.

AI tools struggle with massive codebases. Be specific about which files to focus on and consider isolated development approaches.

7Choosing Tech Stacks

Some technologies work better with AI coding assistants:

TierLanguages/FrameworksAI Effectiveness
ExcellentJS/TS, Python, Rails, React, Next.js90-95% accurate
Very GoodJava, C#, Express, Angular, Django80-90% accurate
GoodGo, Swift, PHP, Flutter, Vue70-80% accurate
ChallengingRust, Kotlin, Elixir, ClojureScript50-70% accurate

For AI-heavy projects, consider "boring" technology stacks with extensive documentation. Productivity gains from AI will be greater, and you'll spend less time correcting code.

Checklist

  • Choose well-established frameworks
  • Be cautious with newer languages (less training data)
  • Prefer modular architectures with small files
  • Break large files into smaller modules

8Beyond Coding

AI tools enhance many aspects of development:

DevOps Automation

Generate CI/CD configs, Dockerfiles, and infrastructure as code.

Documentation

Draft API docs, README files, and technical documentation from code.

Code Explanation

Ask AI to explain complex implementations line by line for onboarding.

Visual Debugging

Share UI bug screenshots. Modern AI tools understand images and suggest fixes.

Checklist

  • Automate DevOps with AI-generated configs
  • Generate documentation from code
  • Use as educational tool for complex code
  • Share screenshots to solve visual bugs

Conclusion

Successful AI coding is about partnership. You bring domain knowledge and critical thinking; the AI brings speed and pattern recognition. The teams who master this partnership will have a significant advantage.

By following these approaches, you'll spend less time fighting with AI tools and more time building better software faster.

Explore Other Guides

Prompt Engineering Guide

Master effective prompting techniques for better AI outputs.

Read the Guide

AI Evaluation Guide

Learn to systematically evaluate AI model performance.

Read the Guide

Test Your Knowledge

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Lightweight coding heuristics and patterns for fast iteration.

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