Common pitfalls and limitations in AI-assisted software development
Core Idea: AI models exhibit systematic blindspots and limitations when applied to software development that developers must recognize and mitigate through specific strategies and practices.
Key Elements
- LLMs bring powerful capabilities but also introduce unique challenges to software development
- Understanding these blindspots enables more effective human-AI collaboration
- Mitigation strategies require adjusting both human practices and model usage patterns
- Blindspots manifest across the entire development lifecycle
Development Process Blindspots
- Stop Digging - Models persistently continue counterproductive approaches rather than pivoting
- Preparatory Refactoring - LLMs attempt changes without preparatory restructuring
- Walking Skeleton - Importance of building minimal end-to-end functionality first
- Bulldozer Method - Using brute force approaches effectively with AI
- Requirements Not Solutions - Need for explicitly stating constraints to guide LLMs
- Mise en Place - Preparing development environments before engaging AI
- Scientific Debugging - Systematic analysis versus random solution attempts
- The Tail Wagging the Dog - When minor concerns dominate important objectives
Technical Implementation Blindspots
- Black Box Testing - LLMs struggle to maintain information hiding boundaries
- Stateless Tools - Challenges with persistent state between invocations
- Use Automatic Code Formatting - Leveraging specialized tools for mechanical tasks
- Keep Files Small - Maintaining manageable file sizes for context limitations
- Use Static Types - Advantages of type systems with LLM assistance
- Use MCP Servers - Standardizing environment interactions for models
- Respect the Spec - Preserving system boundaries that LLMs might change
- Know Your Limits - Recognizing capability boundaries in AI systems
Cognitive and Cultural Blindspots
- Memento - Models lack persistent memory between sessions
- Read the Docs - Importance of providing explicit documentation
- Culture Eats Strategy - Environment influences override explicit instructions
- Rule of Three - LLMs tend toward duplication without explicit refactoring instructions
Connections
- Related Concepts: Human-AI Collaboration, LLM Limitations, Software Engineering Best Practices
- Broader Context: AI-Assisted Development, LLM Application Patterns
- Applications: Development Workflow Optimization, Effective Prompting Techniques
References
- Edward Z. Yang (2025). "AI Blindspots" collection, March 2025.
#ai-development #software-engineering #llm-limitations #best-practices
Connections:
Sources:
- From: Ezyang AI Blindspots