Multimodal RAG for Enhanced Information Retrieval and Generation in Retail

This study explores the use of Multimodal Retrieval-Augmented Generation (RAG) models to enhance information retrieval and generation in retail applications. By combining both structured (e.g., sales data, inventory levels) and unstructured (e.g., product descriptions, customer reviews, images) data sources, RAG models improve the generation of accurate and contextually relevant content. The study evaluates the model's performance on a large-scale retail dataset consisting of product sales data, customer interaction logs, and multimedia content across multiple retail channels. Performance is compared to traditional retrieval methods in terms of accuracy, response quality, computational efficiency, and real-world business impact. Results demonstrate significant improvements in recommendation accuracy 92% and customer engagement metrics. This research contributes to the evolving field of multimodal AI, demonstrating the advantages of hybrid approaches in dynamic business environments and providing practical implementation guidelines for retail organizations.