By the end of this lesson, you will:
You've mastered simple code generation. Now let's unlock the professional features that separate beginners from expert AI developers.
Beginner Level:
"Create a login form"
Professional Level:
"Refactor the authentication system across these 5 files to use JWT tokens,
maintain backward compatibility, and add proper error handling for edge cases"
Scenario: You need to change your API from REST to GraphQL across 15 files.
Cursor Advanced Technique:
Ctrl/Cmd + KRefactor these files to use GraphQL instead of REST:
- Convert REST endpoints to GraphQL mutations/queries
- Update all API calls in components
- Maintain the same functionality
- Add error handling for GraphQL errors
Windsurf Cascade Power:
I want to migrate from REST to GraphQL. Please:
1. Analyze current API structure
2. Design GraphQL schema
3. Update all client calls
4. Test the changes
Modern AI IDEs understand your project structure. They know:
Example: Smart Component Creation
"Create a user profile component that:
- Uses the same styling as UserCard component
- Integrates with our existing auth context
- Follows our form validation patterns
- Connects to the users table structure"
The AI will automatically:
Traditional Debugging:
AI IDE Debugging:
For Runtime Errors:
I'm getting this error: [paste error message]
In this context: [describe what you were doing]
Here's the relevant code: [select problematic code]
My environment: [OS, versions, etc.]
For Logic Errors:
This function should [expected behavior] but instead it [actual behavior].
Test data: [input examples]
Expected output: [what should happen]
Actual output: [what's happening]
For Performance Issues:
This code is running slowly when [specific scenario].
Performance requirements: [response time, memory usage]
Current metrics: [what you're seeing]
Constraints: [limitations to consider]
Create reusable templates for common patterns:
API Route Template:
Create an API route following our standard pattern:
- Path: /api/[resource]/[action]
- Validation using Joi schema
- Database operations with error handling
- Rate limiting (100 req/min)
- Logging with Winston
- Response format: {success, data, error}
React Component Template:
Create a React component following our standards:
- TypeScript with proper interfaces
- Styled-components for CSS
- Error boundaries
- Loading states
- Accessibility (ARIA labels)
- Unit tests with Jest
AI IDEs can recognize and extend architectural patterns:
MVC Pattern:
"I'm using MVC architecture. Create a new feature for blog posts
that follows the same pattern as the existing user management feature."
Microservices Pattern:
"Add a payment service to our microservices architecture.
Follow the same Docker, communication, and error handling
patterns as the user service."
The more context you provide, the better the AI performs:
Poor Context:
Rich Context:
Setup Documentation Context:
Create docs/ai-context.md in your project:
# AI Context Guide
## Architecture
- Frontend: React + TypeScript
- Backend: Node.js + Express
- Database: PostgreSQL
- Auth: JWT tokens
## Conventions
- Components use PascalCase
- Hooks start with 'use'
- API routes in camelCase
- Database tables in snake_case
## Patterns
- All forms use react-hook-form
- API calls through axios interceptors
- Error handling with custom Error class
Reference this in prompts:
Following our conventions in docs/ai-context.md,
create a new user registration flow.
For Projects with 1000+ Files:
Cursor Settings:
{
"cursor.ai.contextLength": 8000,
"cursor.ai.maxTokens": 2000,
"cursor.ai.temperature": 0.1,
"cursor.ai.indexing": "smart"
}
Windsurf Settings:
Batch Operations:
Create these 5 components in one operation:
1. UserList - displays user table
2. UserCard - single user display
3. UserForm - add/edit user
4. UserModal - popup for details
5. UserSearch - filter users
All should follow our design system.
Template Reuse: Save successful prompts as templates:
// Save this pattern
"Create a CRUD interface for [ENTITY] with:
- List view with pagination
- Create/Edit forms with validation
- Delete confirmation
- Search and filters
- Loading states
- Error handling"
Use AI as your pair programming partner:
Code Reviews:
Review this code for:
- Security vulnerabilities
- Performance issues
- Code style consistency
- Potential bugs
- Improvement suggestions
Architecture Decisions:
I need to choose between these approaches for [problem]:
Option A: [describe approach]
Option B: [describe approach]
Consider: scalability, maintainability, team skills
Recommend the best option with reasoning.
Build features incrementally:
Step One: Basic functionality
Create a basic todo app with add/remove
Step 2: Enhanced features
Add to the todo app:
- Mark as complete
- Edit existing todos
- Categories/tags
- Due dates
Step 3: Advanced features
Enhance the todo app with:
- Drag and drop reordering
- Bulk operations
- Keyboard shortcuts
- Offline support
Generate tests before implementation:
Write comprehensive tests for a shopping cart feature:
- Add items to cart
- Update quantities
- Remove items
- Calculate totals with tax
- Apply discount codes
- Persist cart state
Then implement the actual cart functionality.
Phase One: Data Architecture
Design a analytics dashboard data model:
- User activity tracking
- Performance metrics
- Revenue data
- Real-time updates
Create the database schema and API endpoints.
Phase 2: UI Components
Create dashboard UI components:
- Chart components (line, bar, pie)
- Metric cards with trends
- Date range selector
- Export functionality
Use our existing design system.
Phase 3: Integration
Connect the dashboard components:
- Real-time data fetching
- Loading states
- Error handling
- Responsive design
- Performance optimization
Always Review AI-Generated Code For:
Security-Focused Prompts:
Create a secure API endpoint that:
- Validates all inputs
- Uses parameterized queries
- Implements rate limiting
- Logs security events
- Returns minimal error information
Consistency Checks:
Ensure this new code follows our project standards:
- Linting rules (ESLint config)
- Naming conventions
- Error handling patterns
- Documentation requirements
- Testing coverage
You've now unlocked the advanced features that make AI IDEs truly powerful. You can:
Next: Learn how to collaborate with your team using these AI superpowers!