The Double-Edged Sword: Initial Experience and Hidden Pitfalls
As a software engineer, I initially embraced AI-powered editors like Cursor for backend development with great enthusiasm. The ability to automatically generate CRUD operations and complex API logic was intoxicating. However, this convenience came with a hidden cost: a gradual decline in diligence. Over-reliance on AI led me to assume the business logic was inherently correct. This complacency resulted in significant integration issues, such as inconsistent response structures and performance-degrading N+1 queries that only surfaced during high-volume production testing.
Shifting Gears: Mastering AI for Productive Development
To reclaim control, I transitioned from passive code generation to active AI management. I learned to define strict rules within the editor to enforce consistent coding patterns and robust architecture. I also discovered that AI performance degrades with large, monolithic tasks due to token limits, often leading to missing data pointers in DTOs. The solution was to decompose tasks into smaller, manageable pieces and implement a rigorous review process for every change. Using "plan mode" for brainstorming and impact analysis allowed me to vet AI approaches before execution, ensuring smoother deployments and optimized query performance.
