Best Practices for AI-Assisted Development

1. Start with Context, Not Code

The Problem: Many developers jump straight into asking AI to fix specific code without providing adequate context.

The Solution: Always begin by explaining the broader context:

❌ “Fix this modal display issue”

✅ “The modal is opening as a full page instead of displaying as an overlay like it does in our other implementation. Also, the calculations are incorrect and the currency icon is overlapping with input data.”

Why This Matters: AI needs to understand the expected behavior, the current behavior, and the context of your application to provide effective solutions.

2. Use Visual Testing as Your Compass

The Problem: Developers often assume code changes work without visual verification.

The Solution: Always test changes visually, especially for UI-related issues:

– Take screenshots before and after changes

– Test user interactions (clicking, typing, scrolling)

– Verify changes across different screen sizes

– Document visual changes with screenshots

Real Example: In our debugging case, we discovered that CSS changes weren’t applying until we rebuilt the assets. Visual testing revealed this immediately, saving hours of debugging.

3. Break Down Complex Problems Systematically

The Problem: Trying to fix multiple related issues simultaneously often leads to confusion and incomplete solutions.

The Solution: Create a systematic approach:

1. Identify all related issues (modal display, calculations, UI overlap, missing buttons)

2. Prioritize by impact (user experience first, then functionality)

3. Fix one issue at a time with verification after each fix

4. Test the complete solution before moving to the next issue

4. Leverage AI for Pattern Recognition

The Problem: Developers often miss patterns that AI can easily identify.

The Solution: Use AI to:

– Compare implementations between different parts of your application

– Identify inconsistencies in code patterns

– Suggest improvements based on best practices

– Analyze similar issues in your codebase

Example: When fixing calculations, we had AI compare the current implementation with a working version to ensure consistency.

5. Always Verify with Real Data

The Problem: Testing with placeholder or dummy data often misses real-world edge cases.

The Solution:

– Use actual production data (anonymized) for testing

– Test with different data scenarios

– Verify calculations with known correct values

– Test edge cases and error conditions

6. Document Your Process

The Problem: Developers often forget the reasoning behind their changes, making future maintenance difficult.

The Solution:

– Document the problem clearly

– Explain the solution approach

– Note any assumptions or limitations

– Include before/after screenshots

– Update documentation as you go or ask AI to update documentation after every Git commit

– Add this to cursor rules

7. Use AI for Code Review and Quality Assurance

The Problem: Developers often miss subtle issues in their own code.

The Solution: Use AI to:

– Review code for potential issues

– Suggest improvements

– Check for consistency

– Identify security concerns

– Verify best practices

8. Iterate and Refine

The Problem: First solutions are rarely perfect.

The Solution:

– Start with a working solution

– Iterate based on testing results

– Refine based on user feedback

– Continuously improve the implementation

The Systematic Approach in Action

Let’s walk through how we applied these practices to fix a complex modal issue:

Step 1: Context Gathering

– Identified the specific problems (modal display, calculations, UI issues)

– Compared with working implementation

– Understood the expected behavior

Step 2: Visual Testing with MCPs

– Playwright MCP: Automated browser testing to verify modal behavior

– Screenshot Capture: Documented current state and changes visually

– Element Interaction: Tested clicking, typing, and user interactions

– Page State Analysis: Understood the application’s current state

Step 3: Systematic Fixing with Enhanced Tools

– Built-in Codebase Search: Found patterns and compared implementations

– Built-in Terminal Commands: Rebuilt assets when CSS changes weren’t applying

– Code Comparison: Used AI to identify differences between different implementations

Step 4: Verification with MCPs

– Playwright MCP: Automated testing of the complete solution

– Visual Verification: Screenshots confirmed all fixes were working

– Real Data Testing: Used actual data for testing

– Cross-Platform Testing: Verified functionality across different scenarios

Common Pitfalls to Avoid

1. Assuming AI Understands Context

Don’t assume AI knows your application’s architecture, business logic, or user expectations. Always provide context.

2. Skipping Visual Verification

Code changes that look correct in the editor might not work as expected in the browser. Always test visually.

3. Fixing Multiple Issues Simultaneously

This often leads to confusion and makes debugging harder. Fix one issue at a time.

4. Not Testing with Real Data

Placeholder data can hide real-world issues. Always test with actual data when possible.

5. Ignoring User Experience

Technical fixes that don’t improve user experience are incomplete. Always consider the end user.

Key Takeaways

1. Context is King: Always provide comprehensive context when working with AI

2. Visual Testing is Essential: Don’t assume code changes work without visual verification

3. Systematic Approach: Break down complex problems and fix them one at a time

4. Real Data Testing: Always test with actual data, not just placeholders

FAQs

1. What is Cursor AI and how does it help developers?
Cursor AI is an AI coding assistant that provides context-aware suggestions, helps debug complex issues, and enhances productivity with visual testing and systematic analysis.

2. How can I maximize Cursor AI’s effectiveness?
Provide full context, use real data for testing, leverage AI for pattern recognition, and adopt a systematic approach to problem-solving.

3. Can Cursor AI replace human judgment?
No. AI amplifies human capabilities but does not replace human decision-making or understanding of user experience.

4. Are AI-assisted debugging tools reliable for production-level software?
Yes, if used with proper visual verification, real data testing, and systematic validation practices.

5. What are MCPs and how do they complement Cursor AI?
Microsoft Certified Professionals (MCPs) provide specialized tools like Playwright and DevTools for visual testing, automation, and code analysis, enhancing AI-assisted development workflows.