Pattern Recognition

Pattern recognition is the core mechanism that makes AI coding assistants useful. Understanding how AI recognizes patterns helps you leverage this capability effectively.

How Pattern Recognition Works

1. Training — AI learns patterns from millions of code examples
2. Context — Your current code provides local patterns
3. Matching — AI identifies which patterns apply
4. Generation — New code follows recognized patterns

Patterns AI Recognizes

Structural patterns:

• File organization
• Module boundaries
• Import conventions
• Export styles

Naming patterns:

• Variable naming (camelCase, snake_case)
• Function naming conventions
• File naming patterns
• Component naming

Code patterns:

• Error handling approaches
• State management patterns
• API call structures
• Test organization

Leveraging Pattern Recognition

Be consistent: Consistent patterns in your code help AI generate matching code.

Show before asking: Write one example, then ask AI to create similar ones.

Name things clearly: Clear names help AI understand the pattern's purpose.

Group related code: Patterns are easier to recognize when examples are nearby.

When Patterns Help vs Hurt

Help:

• Generating similar components
• Extending existing functionality
• Maintaining code consistency

Hurt:

• When you need to break from patterns
• When existing patterns are poor
• When innovation is needed

For novel solutions, explicitly tell AI to ignore existing patterns.