Various approaches have been used to develop car recommender systems, but there are still limitations in personalizing information. Existing recommender systems often provide less relevant and flexible recommendations because the systems do not better understand users’ needs. Therefore, there is a need for a car recommender system that can offer more personalized recommendations, understand user needs better, and allow for more flexible interactions between the system and the user. This research aims to address these limitations and open up opportunities to enhance the performance and personalization of recommender systems in the automotive field. We developed a car recommender system named Carfin, which utilizes a Large Language Model (LLM) based Conversational Recommender System (CRS) to provide more accurate recommendations that align with user preferences and allow more flexible interactions between the system and the user in the process of getting car recommendations. We performed fine-tuning on the GPT-