When using generative AI models for academic research, one must be aware of a key limitation: their knowledge cut-off. This refers to the latest point in time up to which the model has been trained on data. Beyond this date, the model may lack awareness of significant events, publications, or
Collecting and reviewing relevant literature is often one of the most time-consuming parts of academic research. We tested whether this process could be faster using a single prompt with different generative AI models. To explore this, we prompted each model to return accurate academic articles on ‘legislative backsliding’. The outcome
With generative AI tools becoming increasingly common in academic workflows, many researchers are tempted to use them for tasks like journal selection. But can these models really help you decide where to publish? Our quick test in the field of legislative studies suggests: not just yet. We wanted to see
Classifying gender based on names might initially appear straightforward, yet different AI models vary significantly in how accurately and effectively they handle this task. This blog explores the performance of various models when provided with a structured prompt for name-based gender classification using an uploaded .xlsx file. The evaluation specifically
