ai-dev-guidance

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
    • Be explicit about what is needed and what isn’t
    • Define scope boundaries before discussing architecture
  2. Focus on incremental development
    • Begin with minimal solutions and build up as needed
    • Validate each step before adding complexity
  3. Establish regular check-in points
    • Reconnect to original goals frequently
    • Verify that current direction aligns with requirements
  4. Challenge complexity
    • Ask for justification of complex components
    • Request simpler alternatives when appropriate
  5. Leverage existing tools
    • Start with available libraries and frameworks
    • Question custom implementations of solved problems

How to Use These Guides