This study synthesizes feature-based and pairwise-comparison approaches to item parameter modeling by using an LLM as an investigator of item characteristics and a judge of relative item parameters. The proposed framework aims to improve recovery of item parameter rankings and predictions while providing insight into factors associated with the parameters.