Abstract: From a complementary perspective to model development, data-centric AI aims to improve and analyse data to better understand AI systems. While significant efforts have been made in understanding training data, this talk will introduce some attempts from my group analysing test data. Specifically, I will talk about how to evaluate the difficulty of the test data, or in other words, the model accuracy, in an unsupervised way, where some measurements of model responses are very useful to characterise model performance. Then, I will also introduce a new video format from which motions can be efficiently captured by existing action recognition networks. Finally, I will discuss a new way of prompting large language models, which is zero-shot, task-agnostic, and prompt-specific. I will conclude with perspectives of data-centric problems and AI workflows.