Operationalizing LLMs in Retail: A framework for scalable AI-driven personalization

The retail industry is undergoing a profound transformation driven by the convergence of artificial intelligence (AI) and massive-scale language models. This review examines the operationalization of large language models (LLMs), such as GPT-4 and LLAMA, in the context of scalable AI-driven personalization for retail environments. We present a comprehensive analysis of current architectures, methodologies, and use cases, while introducing the R2P-LLM (Real-time Responsive Personalization using Large Language Models) framework—a five-layer system designed to ensure modular, adaptive, and context-rich personalization. Drawing from experimental results and recent literature, we demonstrate that LLMs significantly outperform traditional and transformer-based systems in key performance areas, including click-through rate, conversion rate, and customer satisfaction. Additionally, the review addresses ethical, infrastructural, and deployment challenges, offering insights into future directions such as on-device inference, explainable AI, and multimodal personalization. The paper concludes that LLMs are not merely enhancements to personalization systems, but foundational technologies for next-generation, experience-driven commerce.