Vibe Coding Best Practices Manual

Vibe Coding represents a revolutionary approach to software development, where artificial intelligence takes on most of the routine work of writing code, while the developer focuses on directing the process and refining the results. To use this approach most effectively, it’s important to follow certain best practices that will help you produce high-quality, reliable, and maintainable code.

Preparation for Development

Detailed Project Specification

Before starting the Vibe Coding process, it’s crucial to create a detailed project specification. This serves as a kind of “blueprint” for the AI, helping it generate code that precisely meets your requirements. A good specification should include:

  • Clear project goals and objectives
  • Database schemas and data structures
  • Description of API endpoints
  • Desired application architecture
  • Technical stack and constraints

You can use other AI models, such as Grok or Claude, to help you structure and detail your requirements. This specification then becomes the foundation that you upload to Vibe Coding tools, such as Cursor or WindSurf, for initial code generation.

Establishing Clear Coding Rules

Define a consistent technical stack and coding patterns in advance. Provide the AI with a rules file that lists:

  • Preferred frameworks, libraries, and tools
  • Style and design patterns
  • Workflow constraints
  • Code formatting and documentation rules

These rules anchor the AI to your technical stack and prevent unwanted deviations or technology switches.

Development Process

Iterating in Small Steps

One of the key principles of successful Vibe Coding is working with small, manageable tasks. Instead of asking the AI to create an entire system at once, break down development into small steps:

  1. Describe a specific function or fix
  2. Let the AI implement it
  3. Test the result
  4. Make adjustments if necessary

This step-by-step approach minimizes complex errors and context problems. For example, it’s better to say “Add an endpoint for logging in” than “Build the entire authentication system.” Small, targeted requests keep the AI on the right track and simplify debugging if something goes wrong.

Prioritizing Testing and Debugging

Treat AI-generated code with the same rigor as human-written code. After each change:

  • Run comprehensive tests frequently to identify problems early
  • If tests fail, ask the AI to debug by asking: “Explain why test X failed and suggest a fix”
  • Ensure that AI-proposed fixes address the root cause without introducing new errors
  • Avoid using placeholders or mocks in production code

Using Version Control and Checkpoints

Regularly commit code generated by AI. Frequent commits with meaningful messages make it easy to roll back if AI changes go wrong. Maintain branch isolation for experiments—you can even run multiple Cursor windows on different branches in parallel.

Save chat histories or transcripts of interactions with AI. They serve as a record of reasoning if you need to retrace steps or understand why code was written in a certain way.

Prompt Engineering for Vibe Coding

Be Specific and Descriptive

Vague requests yield unpredictable results. Clearly specify what you want and how. Include relevant details such as function names, data formats, or user stories.

For example, instead of “Make this better,” say “Optimize the getUserData function for speed, possibly by reducing API calls or caching results.”

Use Role and Task Instructions

Many AI coding tools allow you to set a role for the assistant or context. For example, start with: “You are a Python backend expert following PEP8 standards.” Then give the assignment: “Add input validation to the user registration function following these standards.” This context tunes the AI to respond with relevant knowledge and style.

Use AI for Plans and Explanations

Don’t just ask the AI to write code. Ask it to outline a solution first.

Planning request: “Create a step-by-step plan for implementing function X. Don’t write code yet—just outline the approach (data structures, functions, error handling).”

Once you approve the plan, request: “Great, now implement step 1.” This chain-of-thought style of prompting keeps the AI focused and allows you to identify design issues early.

Choosing the Right Tools and Models

Selecting the Appropriate AI Model

Different AI models excel at different tasks. Here’s how they compare for Vibe Coding:

  1. Claude 3.7 Sonnet (thinking mode) — best suited for structured coding, function calls, and tool integration.
  2. Grok 3 — useful for creating detailed project specifications before starting coding.
  3. GPT-4, DeepSeek, LLaMA, Mistral — alternative models that work well for AI-assisted coding but may vary in reliability and context handling.

Using Custom AI Models in Cursor

Cursor IDE allows you to integrate custom AI models, which means you can:

  1. Override the OpenAI API key in settings.
  2. Use alternative models, such as Grok, that support the OpenAI API format.
  3. Experiment with different models to find the one that best suits your coding style.

Workflow Optimization

Choosing the Right Execution Mode

  1. Manual mode — AI requires approval for each change.
  2. Automatic mode — AI decides which actions require approval.
  3. YOLO mode — AI automatically executes all changes (not recommended for production).

Tips for Speed and Efficiency

  1. Run multiple branches in separate Cursor windows for parallel feature development.
  2. Claude 3.7 (thinking mode) provides more accurate results but may work more slowly.
  3. Use lighter models for quick refactoring and small fixes.

Avoiding Common Mistakes

AI Breaks Other Functions When Fixing Errors

  • Solution: Clearly instruct the AI to modify only the requested section.
  • Solution: Run tests to ensure existing functions remain unchanged.

AI Overwrites .env Files or API Keys

  • Solution: Explicitly define rules prohibiting the AI from modifying environment variables.

AI Generates Unnecessary Test Data

  • Solution: Limit the use of test data to test environments only.

AI Takes Too Long to Make Changes

  • Solution: Keep the AI context size small and regularly start new chats.

Conclusion

Vibe Coding is a powerful approach to software development that can significantly accelerate the code creation process and make it more accessible. By following these best practices, you can maximize the effectiveness of AI capabilities to create high-quality, reliable, and maintainable code. Remember that AI is a tool that should complement your skills and knowledge, not replace them entirely. The right combination of human expertise and AI capabilities can lead to impressive results in software development.

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