AI Coding Assistant Antipatterns

Common problematic patterns to watch for when working with AI coding assistants, and practical strategies to address them.

Quick Reference Guide

1. Premature Architecture Complexity - Creating overly complex architectures before requirements are clear

2. Test-Driven Design Misapplication - Following test patterns blindly instead of designing from first principles

3. Purpose Drift During Refactoring - Losing sight of original goals during continuous refactoring

4. Library and Framework Reinvention - Reimplementing functionality already available in established libraries

5. Failure to Separate Concerns - Mixing different responsibilities within the same components

General Strategies

When working with AI coding assistants:

While each antipattern has specific remediation approaches, several general strategies apply across all patterns:

1. Start with clear requirements and constraints
2. Be explicit about what is needed and what isn't

3. Define scope boundaries before discussing architecture

4. Focus on incremental development

5. Begin with minimal solutions and build up as needed

6. Validate each step before adding complexity

7. Establish regular check-in points

8. Reconnect to original goals frequently

9. Verify that current direction aligns with requirements

10. Challenge complexity

11. Ask for justification of complex components

12. Request simpler alternatives when appropriate

13. Leverage existing tools

14. Start with available libraries and frameworks
15. Question custom implementations of solved problems

How to Use These Guides

• Reference a specific antipattern when you detect it
• Apply the suggested interventions to redirect the AI assistant
• Use the preventive measures when starting new projects
• Add your own observations and successful strategies