The prototype-based concept grounding approach, while effective in minimizing concept shift across domains, may have limitations when dealing with more diverse and complex datasets. One potential limitation is the scalability of the prototype bank as the number of classes or concepts increases, leading to higher computational costs and memory requirements. To address this limitation, the approach can be further improved by implementing a more efficient prototype selection mechanism that prioritizes the most informative prototypes for each concept. Additionally, incorporating a mechanism for prototype refinement through iterative updates based on new data samples can enhance the adaptability of the prototypes to diverse and complex datasets. Furthermore, exploring techniques for dynamic prototype management, such as prototype aging or consolidation, can help maintain the relevance of prototypes over time and across different data distributions.