PRACTICAL GUIDE

Vibe Coding: The No-Nonsense Guide to Coding with AI

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Let's face it: coding with AI can feel like trying to drive a rocket-powered car when you've only ever used a bicycle. It's powerful, but the learning curve is steep, and the results can be... unpredictable. This guide distills what actually works from hands-on experience, without the theoretical fluff.

In This Guide

Who Is This Guide For?

This guide is for developers who are tired of theoretical advice about AI coding that doesn't translate to real-world projects. Whether you're just starting to use GitHub Copilot or you're trying to build complex features with Claude or GPT, you'll find practical approaches that have been battle-tested on actual projects.

📝The Smart Planning Process

The difference between AI-assisted success and a hot mess often comes down to your planning approach. Here's what actually works:

Start with a Detailed Implementation Plan

Having the AI write up the detailed implementation plan first (in markdown) gives you a significant head start. It helps clarify requirements and identifies potential issues before writing a single line of code.

Example Implementation Plan Prompt
I need to build a user authentication system for a React/Node app with MongoDB. 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 6. Security considerations

Review & Refine Before Coding

Don't accept the first plan. Review it with a critical eye. Delete unnecessary complexity, mark features as "won't do" if they seem too intricate, and keep the scope tight.

Real-world Example

When our team built an invoice management system, we initially had the AI generate a plan with 12 features. After review, we marked 5 as "v2" and removed 2 entirely. This focused approach meant we delivered v1 in half the originally estimated time, with significantly fewer bugs.

Implement Section-by-Section

Work through one logical section at a time rather than attempting to build everything at once. Have the AI generate code for a specific component, review it, implement it, then move on.

Create a "won't do" list alongside your "to-do" list. It's just as important to decide what you're NOT implementing as what you are. This keeps your AI-assisted coding focused and prevents scope creep.

Quick Checklist

  • Create a comprehensive implementation plan in markdown
  • Review and refine to remove unnecessary complexity
  • Implement section by section with focus
  • Track progress by having AI mark sections as complete
  • Commit each working section to Git before moving on
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Mastering Version Control for AI Coding

AI-generated code requires a different approach to version control. Here's how to avoid disaster:

Use Git Religiously

Don't trust the AI tools' revert functionality. It's unreliable at best. Commit more often than you normally would, with smaller, logical changes.

Clean Start Approach
# Start each feature with a clean slate git checkout -b feature/user-authentication 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 goes down a rabbit hole, don't be afraid to use git reset --hard HEAD. This is especially useful when the AI starts generating increasingly complex solutions to fix its own bugs.

Avoid Cumulative Problems

Multiple failed attempts will create layers of bad code. When the AI starts building workarounds for its previous incorrect implementations, it's time to reset and start fresh.

When I Almost Quit Coding

I once spent 3 hours letting Claude enhance a feature, fix bugs, and refactor... without committing. The codebase became so convoluted that I couldn't tell what was happening anymore. I lost all that work and had to start over. Now I commit after each meaningful change, and I'm never caught in that situation again.

AI agents are notoriously bad at dealing with complex merge conflicts. If you hit complex merge scenarios, handle the conflict resolution manually rather than asking the AI to solve it. The AI will often try to incorporate both versions, leading to duplicated code with subtle bugs.

Quick Checklist

  • Start each feature with a clean Git branch
  • Commit small, logical changes frequently
  • Use git reset when the AI goes down a rabbit hole
  • Implement the final solution on a clean codebase
  • Handle merge conflicts manually, not with AI

🧪Testing Strategies That Actually Work

AI is great at writing code but terrible at imagining how users break it. Here's how to test effectively:

Focus on Integration Tests

AI is actually pretty good at writing unit tests, but they rarely catch the real issues. Focus on end-to-end integration tests that verify user journeys through your application.

Integration Test Example
// This tests the actual user flow, not just isolated functions describe('User Authentication Flow', () => { test('User can register, verify email, and log in', async () => { // 1. Register a new user const registerResponse = await registerUser({ email: 'test@example.com', password: 'SecurePassword123!' }); expect(registerResponse.success).toBe(true); // 2. Verify email (simulate) const verifyResponse = await verifyEmail(registerResponse.verificationToken); expect(verifyResponse.success).toBe(true); // 3. Log in with new credentials const loginResponse = await loginUser({ email: 'test@example.com', password: 'SecurePassword123!' }); expect(loginResponse.success).toBe(true); expect(loginResponse.user).toBeDefined(); expect(loginResponse.token).toBeDefined(); }); });

Simulate Real User Behavior

Test features by simulating someone actually clicking through your site/app. AI often misses critical user paths because it's focused on the "happy path."

Catch Regressions in Logic

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

The Mysterious Bug

We had GPT-4 refactor our payment processing code to improve performance. Everything looked perfect and unit tests passed. But it had changed how we rounded currency values in a subtle way that only appeared in real transactions. Now we always include tests with real-world financial calculations that would catch these issues.

Use Tests as Guardrails

Some teams have found success by writing tests first, then having the AI implement features that pass those tests. This approach provides clear boundaries for what the AI should build.

Create a "test-first" template for your team that outlines acceptance criteria and edge cases before asking the AI to generate code. This ensures everyone is aligned on what successful implementation looks like.

Quick Checklist

  • Prioritize integration tests over unit tests
  • Test user journeys by simulating clicks through the site
  • Write tests that would catch AI's tendency to modify unrelated logic
  • Consider writing tests before having the AI implement features
  • Include edge cases that reflect real-world usage

🐛No-Nonsense Bug Fixing

When bugs appear in AI-generated code, here's how to fix them efficiently:

Leverage Error Messages

Simply copy-pasting the exact error message is often enough for the AI to identify and fix issues. Include the stack trace and relevant code snippets for better results.

Effective Bug Fixing Prompt
I'm getting this error in my React component: TypeError: Cannot read property 'filter' of undefined at UserList.render (src/components/UserList.js:24:43) Here's the component code: import React from 'react'; class UserList extends React.Component { constructor(props) { super(props); this.state = { searchTerm: '' }; } render() { const { searchTerm } = this.state; const filteredUsers = this.props.users.filter(user => user.name.toLowerCase().includes(searchTerm) ); return ( <div> <ul> {filteredUsers.map(user => ( <li key={user.id}>{user.name}</li> ))} </ul> </div> ); } } // Usage: <UserList /> (without passing users prop) What's causing this error and how do I fix it?

Analyze Before Coding

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

Reset After Failures

If a fix attempt doesn't work, start with a clean slate rather than building on the failed approach. This prevents compounding issues.

The Five-Solution Approach

When debugging a particularly tricky API integration issue, we asked ChatGPT to generate five different solutions, each with a different underlying assumption about the cause. Solution #3 worked perfectly because it addressed the actual issue (a timing problem with token refresh) rather than the more obvious but incorrect paths.

Implement Strategic Logging

Have the AI add strategic logging to help understand what's happening when bugs are hard to reproduce. This is especially helpful for asynchronous operations and state management issues.

Try Different AI 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 the AI suggests rewriting entire modules or adding multiple dependencies to fix a small bug, it's a sign you should take a step back and reassess.

Quick Checklist

  • Provide complete error messages with stack traces
  • Ask for analysis of multiple possible causes
  • Reset to a clean approach after failed fix attempts
  • Add strategic logging to understand complex issues
  • Try different AI models when one gets stuck
  • Implement fixes on a clean codebase
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🛠️Optimizing Your AI Tools

Getting the most out of AI coding tools requires some specific approaches:

Create Instruction Files

Store detailed instructions in appropriate files (cursor.rules, windsurfruler.claude.md) to ensure consistent AI behavior across your project.

Example windsurfruler.claude.md file
# Project Guidelines for Claude ## Code Style - Use TypeScript for all new files - Follow React functional component pattern with hooks - Use named exports rather than default exports - Use Tailwind CSS for styling ## Naming Conventions - Components: PascalCase - Hooks: camelCase with 'use' prefix - Utilities: camelCase - Types/Interfaces: PascalCase with descriptive names ## Project Structure - Place components in /components directory - Place hooks in /hooks directory - Place types in /types directory - Follow atomic design principles for component organization ## Architectural Constraints - Use React Context for global state - Avoid excessive prop drilling - Keep components under 150 lines - Follow container/presentation pattern for complex components

Keep Local Documentation

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

Use Multiple Tools Simultaneously

Some developers run both Cursor and WindSurf simultaneously on the same project. Each has strengths for different tasks.

The Best of Both Worlds

I keep Cursor open for rapid frontend iterations - it's fantastic at suggesting component improvements and fixing UI issues. But for complex backend work and database queries, I switch to WindSurf with Claude which gives me more thorough explanations and catches edge cases Cursor might miss.

Tool Specialization

Cursor is generally faster for frontend work, while WindSurf (with Claude) tends to think longer but produce more robust solutions for complex problems.

Compare Outputs

For critical features, generate multiple solutions and compare them. This helps identify the best approach and catches potential issues that a single solution might miss.

Creating a project-specific prompt library can dramatically improve your AI output quality. Keep a document with proven prompts for common tasks like "Create a new React component", "Write a database migration", or "Implement form validation". Refine these prompts over time as you learn what works best.

Quick Checklist

  • Create detailed instruction files for consistent AI behavior
  • Keep local copies of API documentation for reference
  • Use different AI tools for their respective strengths
  • Generate multiple solutions for critical features
  • Maintain a library of effective prompts

🏗️Building Complex Features Without Frustration

Complex features require a different approach with AI tools:

Create Standalone Prototypes

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

Example Prototyping Approach
// 1. Create a minimal sandbox project npx create-react-app ai-prototype // 2. Install only the dependencies needed for this feature cd ai-prototype npm install chart.js react-chartjs-2 // 3. Have the AI build the feature in isolation // 4. Once it works correctly, integrate into the main project

Use Reference Implementations

Point the AI to working examples of similar functionality, whether from your own projects or well-documented open source examples.

The Critical Reference

We were struggling to implement a complex drag-and-drop interface with nested sortable elements. After three failed attempts with different AI tools, we found a GitHub repo with a similar implementation. Once we showed that to Claude, it produced a working solution in one try that needed minimal adjustments.

Define Clear Boundaries

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

Use Modular Architecture

Service-based architectures with clear boundaries work better than monorepos for AI-assisted development of complex features.

AI tools struggle with massive codebases. If your project is large, be very specific about which files the AI should focus on and consider using isolated development approaches like feature branches or serverless functions to minimize the context the AI needs to understand.

Quick Checklist

  • Build complex features in isolation first
  • Provide reference implementations when available
  • Define clear boundaries for what the AI can modify
  • Prefer modular architectures with clear interfaces
  • Break complex features into smaller, manageable units

🧩Choosing the Right Tech Stack

Some technologies work much better with AI coding assistants than others:

Established Frameworks Excel

Ruby on Rails works exceptionally well with AI tools due to 20 years of consistent conventions. Other well-established frameworks like Django, Express, and .NET Core also perform well.

Training Data Matters

Newer languages like Rust or Elixir may have less training data, resulting in less accurate code suggestions. Consider this when choosing technologies for AI-assisted projects.

A Learning Lesson

Our team tried to build a high-performance service with Rust and GPT-4. While the code looked reasonable at first glance, it contained subtle issues around memory safety and async patterns that took a senior Rust developer days to fix. For our next project, we chose Go instead, which the AI handled much more reliably due to its simpler syntax and larger presence in training data.

Modularity Is Key

Small, modular files are easier for both humans and AIs to work with. Microservices and serverless architectures often work well with AI assistance.

Avoid Large Files

Files with thousands of lines are difficult for AI tools to process effectively. Break down large files into smaller, more focused modules.

When building new projects that will rely heavily on AI assistance, consider using "boring" technology stacks with extensive documentation and established patterns. The productivity gains from AI will be greater, and you'll spend less time correcting AI-generated code.

AI-Friendly Tech Stack Tiers

TierLanguages/FrameworksAI Effectiveness
ExcellentJavaScript/TypeScript, Python, Ruby on Rails, React, Next.js90-95% accurate code, minimal corrections needed
Very GoodJava, C#, Express.js, Angular, Django, Laravel80-90% accurate code, occasional corrections
GoodGo, Swift, PHP, Flutter, Vue.js70-80% accurate code, regular corrections
ChallengingRust, Kotlin, Elixir, ClojureScript, Svelte50-70% accurate code, significant corrections

Quick Checklist

  • Choose well-established frameworks with consistent conventions
  • Be cautious with newer languages that have less training data
  • Prefer modular architectures with small, focused files
  • Break large files into smaller modules
  • Consider "boring" technology for maximum AI effectiveness
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🚀Beyond Just Coding

AI tools can enhance many aspects of development beyond just writing code:

DevOps Automation

Use AI to generate CI/CD configurations, Dockerfiles, and infrastructure as code. These are often repetitive and follow patterns that AI excels at learning.

DevOps Prompt Example
Generate a GitHub Actions workflow file for a TypeScript Node.js application with the following requirements: 1. Run on push to main and pull requests 2. Node version: 16.x 3. Steps: - Install dependencies - Run ESLint - Run unit tests with Jest - Build the application - Deploy to staging on push to main (using a GitHub secret for AWS credentials) - Skip deployment on pull requests

Design Assistance

AI tools can generate favicons, UI mockups, and other design elements when given the right instructions.

Content Creation

Use AI to draft documentation, API descriptions, marketing materials, and more. This is especially effective when based on your actual code.

Documentation Magic

Instead of spending hours writing API documentation, we had Claude analyze our Express routes and generate comprehensive OpenAPI documentation in minutes. After a quick review and some minor adjustments, we had professional-quality docs that would have taken days to create manually.

Educational Tool

Ask the AI to explain complex code implementations line by line. This is great for onboarding new team members or learning new frameworks.

Use Screenshots

Share UI bugs or design inspiration visually. Modern AI tools can understand screenshots and suggest implementation fixes.

Create code explanation documentation by having the AI explain complex parts of your codebase in simple terms. This creates an invaluable resource for new team members and helps preserve knowledge when key developers leave the project.

Quick Checklist

  • Automate DevOps with AI-generated configurations
  • Generate design assets like favicons and mockups
  • Create documentation and marketing materials
  • Use as an educational tool for complex implementations
  • Share screenshots to solve visual bugs quickly

📈Getting Better Every Day

Continuous improvement is key to effective AI-assisted development:

Regular Refactoring

Once tests are in place, refactor frequently. AI tools excel at suggesting code improvements and applying consistent patterns.

Identify Refactoring Opportunities

Ask the AI to analyze your codebase for refactoring candidates. This can uncover patterns and inconsistencies you might miss.

Refactoring Analysis Prompt
Please analyze these components and identify refactoring opportunities: 1. Look for repeated code patterns that could be extracted 2. Identify inconsistent naming conventions 3. Suggest component restructuring for better maintainability 4. Highlight potential performance issues 5. Suggest ways to improve readability For each suggestion, please explain the benefit and provide a brief code example of the improved approach.

Stay Current

Try each new model release. AI capabilities are evolving rapidly, and newer models often solve problems that older versions struggled with.

Model Evolution

A complex data visualization feature that GPT-3.5 couldn't handle at all was implemented almost perfectly by GPT-4. Six months later, when we needed to enhance it, Claude 3 Opus provided a solution that was even more elegant and performed better. The rate of improvement is stunning.

Recognize Strengths

Different models excel at different tasks. Keep track of which models perform best for your specific use cases.

Create a "model strengths" document for your team that tracks which AI models perform best for different tasks. For example: "Use Claude for detailed code explanations and architectural planning, GPT-4 for refactoring and bug fixing, and Copilot for rapid code completion during initial implementation."

Quick Checklist

  • Refactor code once tests are in place
  • Ask AI to identify refactoring opportunities
  • Try each new model release
  • Track which models excel at specific tasks
  • Share effective prompts and approaches with your team

Conclusion: It's About Partnership

The most successful AI coding isn't about replacing your skills—it's about creating an effective partnership. You bring domain knowledge, critical thinking, and real-world context, while the AI brings speed, pattern recognition, and breadth of knowledge.

By following the practical approaches in this guide, you'll spend less time fighting with AI tools and more time leveraging them to build better software faster. The teams and individuals who master this partnership will have a significant competitive advantage in the years ahead.

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