AI tools enable people to produce work outside their domain expertise, but functional correctness doesn't equal domain quality. Code runs, tests pass, interfaces render — yet architectural considerations, edge case handling, and maintainability concerns are missing. This leads to technical debt accumulation, knowledge fragmentation, review fatigue, and false velocity. The solution isn't restricting AI use but applying critical thinking to outputs, involving domain experts when working outside your expertise, using MCP tools to ground AI in domain-specific knowledge, and recognizing when you can't properly evaluate what AI generates.
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