Developed a warning mechanism within the google/meridian repository to enhance model interpretability and practitioner safety. The work focused on the ModelSpec class, introducing logic to detect and alert users when coefficient priors are applied, thereby flagging potential estimation and interpretability concerns early in the analysis workflow. This feature was implemented using Python and leveraged backend development and data modeling skills, with unit testing ensuring reliability and consistency with existing warning patterns. By surfacing these warnings proactively, the solution supports better decision-making for model validation and reduces the risk of misinterpretation, ultimately improving data quality and user safety in analytical processes.