Process Mining has come a long way to meet the needs of organizations that must optimize their operations. However, its use is still driven by technical users who can interpret process maps, models, graphs and other types of analyses. Business users, on the other hand, frequently report being intimidated by Process Mining tools’ interfaces and not knowing “what to do next”. An alternative to address this issue is providing more fluid and friendly interfaces for non-technical users based on natural language querying. Recent advances in Large Language Models (LLMs) have expanded the horizon for such interfaces. In this work we propose a new strategy to combine LLM capabilities with a framework for a natural language question-and-answer interface to Process Mining, which combines the flexibility of the former with the scalability and precision of the latter. We expand upon previous works in the area to research the dimensions of flexibility, generalization, scalability and precision. Finally, we implement such an LLM-enhanced framework and test it against a real-life compilation of questions to compare the performance of LLM-based, non LLM-based and hybrid implementations and point to directions in this field of research.