Industrial recommendation systems rely on a multi-stage process to serve billions of users, with the final re-ranking step having a disproportionate impact on user engagement and performance. The use of Large Language Models (LLMs) in recommendation systems has gained popularity, but three key gaps hinder their industrial adoption. Researchers have identified the need to address these gaps to improve the efficiency and effectiveness of recommendation systems. The re-ranking step is particularly crucial for carousel and grid display formats, where user engagement is significantly influenced by the final ranking of items. To bridge the gaps, researchers must investigate the application of LLMs in the context of industrial recommendation systems, focusing on the re-ranking step1. This matters to practitioners because optimizing the re-ranking step can lead to significant improvements in user engagement and downstream performance, ultimately driving business success.