A troubling pattern is emerging across technology teams: organizations deploying large language models lack fundamental understanding of how these systems actually work or perform. Recent discussions on developer forums reveal widespread confusion about AI fundamentals, with even senior engineers struggling to articulate basic concepts. This knowledge gap creates blind spots in production environments, where teams deploy LLM-powered features without proper evaluation frameworks. The problem is particularly acute for JavaScript developers and other engineers pivoting toward AI work, who face an overwhelming and often contradictory landscape of resources and courses.