IT Engineer's Experience with AI-Assisted Development Reveals Common Pitfalls

From Vibe Coding to Understanding Software Architecture

An IT engineer with a background in systems engineering and automation recently shared their experience transitioning to AI-assisted full-stack development. Starting with "vibe coding" using AI tools, they initially built scripts and then progressed to full applications without formal software engineering training.

While AI-generated code worked initially, significant architectural problems emerged as applications grew:

• Excessive client-side data pulling leading to large payloads and slow page loads
• Lack of clear separation between client and server logic
• Unstructured database queries without proper organization
• Unexpected behavior with Row-Level Security (RLS) implementations
• Client-side data aggregation causing inconsistencies
• General architectural drift and increasing difficulty with debugging and maintenance

The engineer notes these issues didn't appear immediately but became obvious as applications scaled. They emphasize that while AI tools can generate functional code, they often miss the architectural decisions and trade-offs that experienced developers consider. This experience led them to treat AI more like a junior developer requiring supervision rather than a fully reliable solution.

Coming from IT infrastructure, the engineer gained new appreciation for the complexity of software engineering decisions, particularly around maintainability, security, and clean architecture. Their experience highlights the gap between functional code and production-ready systems.