Why AI Outputs Are Only as Good as the Questions You Know to Ask

A developer essay published on DEV Community argues that AI model outputs are constrained not by the model's capability but by the quality and completeness of the user's prompt. The author illustrates this with a coding example where an AI correctly built an email verification feature, yet left critical security gaps — such as no code expiry and no brute-force protection — simply because those requirements were never specified. The piece distinguishes between 'known unknowns' and 'unknown unknowns,' noting that true expertise lies in recognising which questions need to be asked before work begins. The author contends that AI tools can sharpen this blind spot by producing fluent, seemingly complete output that masks missing requirements until they cause real problems. As a proposed solution, the author describes building a lightweight tool called 'jev' designed to flag potential prompt blind spots without the latency or friction of existing prompt-review approaches.