Time series analysis is a powerful tool for researchers across disciplines, yet its reliance on programming can pose a challenge for those without coding expertise. This blog demonstrates how AI-driven prompts can enable researchers to perform simple time series analyses without writing a single line of code, making data insights
Data pre-processing is a crucial but often tedious stage in research, particularly when standardising complex CSV datasets. This blog demonstrates how GenAI can systematically structure geographic location data directly from an input CSV file without coding. Our primary objective was to ensure consistent and accurate categorisation, preparing the data for
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
As part of testing the synthetic data generation capabilities of GenAI models, we assigned a seemingly simple task to some of the most popular language models: to generate 50 unique customer feedback entries about a smartwatch, including specified fields (name, age, city, feedback, and date of purchase) and a varied
Named Entity Recognition (NER) is a key technique in natural language processing that involves identifying and classifying specific entities in a text, such as people, organisations, locations or dates. The following prompt offers a structured and nuanced approach to testing NER capabilities using a rich biographical narrative. The prompt instructs
