Large Language Models (LLMs) are increasingly deployed in electronic commerce to automate product description generation at scale. Despite their growing adoption in production systems, limited evidence exists on how variations in AI-generated descriptions influence consumer behavior in real-world commercial settings. The study contributes an approach for integrating behavioral analytics into AI-driven content generation systems and highlights adaptive prompt optimization as a practical decision-support mechanism in contemporary digital commerce. This study proposes a behaviorally grounded, closed-loop framework for optimizing AI-generated product descriptions within a production e-commerce environment. Prompts are treated as configurable system-level controls whose effects are empirically evaluated using behavioral feedback. Latent content attributes are extracted from generated descriptions via topic modeling and linked to user clickout behavior using regression-based analysis with confidence intervals. The resulting estimates guide systematic prompt adaptations, which are validated through online A/B experimentation.