AI is changing the way we write code. Instead of just assisting with small snippets, AI can now take on an entire project—writing, debugging, and even testing the code while developers guide the process. This approach, often called Vibe Coding or Agentic Coding, allows AI to handle most of the heavy lifting, enabling developers to focus on refining and directing the project.
This guide summarizes key insights on how to make the most of AI-powered coding, particularly in Cursor and WindSurf IDEs, while ensuring the results are reliable and maintainable.
Choosing the Right AI Model
Different AI models excel at different tasks. Here’s how they compare for Vibe Coding:
- Claude 3.7 Sonnet (Thinking mode) – Best for structured coding, function calling, and tool integration.
- Grok 3 – Useful for generating detailed project specifications before coding begins.
- 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 custom AI model integration, meaning you can:
- Override the OpenAI API Key in settings.
- Use alternative models like Grok, which supports OpenAI’s API format.
- Experiment with different models to find the best fit for your coding style.
The Importance of AI Coding Rules
A key lesson from using AI for coding is that AI needs clear guidelines to ensure high-quality output. Without structured rules, AI can:
- Use the wrong technologies (e.g., switching from SQL to JSON storage unexpectedly).
- Introduce duplicate code instead of reusing existing functions.
- Break unrelated parts of the project while fixing bugs.
- Generate too much mock data, making production unreliable.
To prevent these issues, developers can define specific AI coding rules.
Key AI Coding Rules for Better Code Quality
By setting clear rules, developers can ensure AI follows correct development patterns.
General Code Quality Guidelines
- Favor simple solutions over unnecessary complexity.
- Avoid code duplication—make sure AI reuses existing functions.
- Check for existing logic before adding new code.
Environment-Specific Rules
- Keep separate environments for development, testing, and production.
- AI should not modify .env files to prevent accidental resets of API keys.
- Ensure database configurations remain stable across environments.
AI Behavior Rules
- AI should only make requested changes—no unnecessary refactoring.
- Avoid introducing new technologies or patterns unless absolutely necessary.
- Large files should be refactored before exceeding 200–300 lines for maintainability.
Data Handling Rules
- Mock data should be limited to test environments only.
- AI should use SQL databases rather than storing data in JSON files.
- The AI should strictly follow the chosen tech stack.
Setting Up Rules in Cursor
If you’re using Cursor IDE, you can configure AI coding rules in a few steps:
- Open Cursor IDE.
- Go to Settings → Rules.
- Create a cursor/rules/ folder to define project-specific guidelines.
- AI will now follow your coding preferences and workflow.
AI-Driven Coding: Step-by-Step Process
1. Generate a Detailed Project Specification
Start by using Grok 3 or Claude 3.7 to create a comprehensive project specification. A good spec includes:
- Tech stack
- Database schema
- API endpoints
2. Load the Specification into Cursor
- Open a new Cursor project.
- Paste the specification into the AI chat window.
- Let AI generate the code based on the provided details.
3. Monitor AI’s Code Generation
- AI will generate entire files and features.
- Apply rules to ensure the AI follows best practices.
- Keep AI’s context window small to avoid performance slowdowns.
4. Testing & Quality Assurance
- End-to-end testing is more effective than unit tests.
- After every change, run tests to prevent regressions.
- AI should automatically generate test cases for all new functionality.
Optimizing the AI Coding Workflow
Choosing the Right Execution Mode
- Manual Mode – AI requires approval for every change.
- Auto Mode – AI decides which actions need approval.
- YOLO Mode – AI executes all changes automatically (not recommended for production).
Speed & Efficiency Tips
- Run multiple branches in separate Cursor windows to develop features in parallel.
- Claude 3.7 (Thinking mode) provides more accurate results but can be slower.
- Use lighter models for quick refactoring and small fixes.
Common Pitfalls & How to Avoid Them
Here are some common challenges and their solutions:
AI Breaks Other Features While Fixing Bugs
- Solution: Clearly instruct AI to only modify the requested section.
- Solution: Run tests to ensure existing features remain unaffected.
AI Overwrites .env Files or API Keys
- Solution: Explicitly define rules to prevent AI from modifying environment variables.
AI Generates Unnecessary Mock Data
- Solution: Restrict mock data usage to test environments only.
AI Takes Too Long to Execute Changes
- Solution: Keep AI context size small and start fresh chats regularly.
Additional AI Tools to Enhance Development
The speaker also mentioned Mammoth AI, a $10/month tool that grants access to:
- Claude, GPT-4, DeepSeek, LLaMA, Mistral, Grok for coding.
- MidJourney, Stable Diffusion, and DALL·E for image generation.
These models can act as coding agents within Cursor and WindSurf, further improving the AI-assisted development process.
Final Thoughts
After spending over 100-150 hours coding with AI agents, the speaker concluded:
- AI-powered coding is highly effective when paired with well-defined rules.
- It works best for full projects, rather than small snippets.
- Success depends on clear workflows and rigorous testing.
- AI speeds up development but still requires human oversight to ensure code quality.
While Vibe Coding isn’t perfect yet, it’s evolving quickly. As AI tools improve, mobile-friendly coding agents may become the next big innovation in software development.
Key Takeaways
- AI can generate entire applications, but it needs strict rules to maintain code quality.
- Cursor & WindSurf + Claude 3.7 (Thinking mode) offer the best experience.
- Testing, version control, and workflow rules are essential for managing AI-generated code.
- Developers still play a critical role—AI can assist, but human oversight ensures success.
With the right tools and strategies, AI can transform how we build software, making development faster and more efficient than ever before.
