Mechanism of AI Content Output and Review: Functional Positioning Based on the Triangular Prism Tower and Double Helix Model

AI-generated content currently faces three major challenges: insufficient output reliability, lack of review mechanisms, and poor quality of generated content. The common root of these problems lies in the absence of a built-in dual-track system for "pre-output review" and "post-output tracking" in AI's underlying mechanisms. This paper deconstructs the AI generation process into three stages—pre-output (original intention), review mechanism, and output content (final presentation)—and proposes the Triangular Prism Tower and Double Helix Model. As the core of the review mechanism, the Triangular Prism Tower Model implements three tests—the Cost Gate, Risk Gate, and Consistency Gate—at the pre-output stage. The Double Helix Model, as an alignment mechanism, achieves dynamic balance between personalized expression and system security in post-output tracking and feedback. This paper demonstrates the collaborative operation of these two models and their path to addressing three major technical blind spots in current AI: unreliable pre-output, lack of review mechanisms, and low-quality output content. Unlike post-hoc fine-tuning methods, the proposed framework achieves native reliability through built-in logic gates. This study provides a locatable, evaluable, and implementable underlying mechanism framework for the evolution of AI from "high generation capability" to "high reliability."