Machine learning (ML) models are used increasingly in high-stakes areas such as health and finance because of their strong performance. However, having good performance in metrics such as accuracy or the f1 score alone is not all that is important as trust is also essential in these areas. TalkToModel is a system that addresses this challenge, using a large language model (LLM), by letting the users interact with their models through natural language. However, this system only allows a user to ask black-box questions. Another way to achieve trust is through models that are constructed with interpretability in mind that a human can understand. Genetic programming (GP) models are such models that have the potential to be interpreted. This thesis investigates if GP models can be made even more interpretable using TalkToModel. To do this an enhanced version of TalkToModel called TalkToGP is created with three main contributions: 1) Integration of GP Models into TalkToModel, 2) the ability to ask GP modelspecific questions and 3) the ability to do a comparative analysis between multiple GP models. This system is built using the feedback from GP users who gave insights on their experience with GP as well as their wishes for this system. In the end, the system is evaluated by GP users in an experiment. The experiments showed that the enhanced version of TalkToModel shows a strong indication that it increases the interpretability of GP models. This means the system could be a useful tool for anyone working with GP models.