In our earlier posts, we explored how useful existing Custom GPTs are for academic tasks and explained how to create your own GPT from scratch. This follow-up post puts those insights into practice by introducing QuantiCheck—a Custom GPT we developed specifically to assess the methodological rigour and reproducibility of
Generative AI is not only useful for content creation – it is increasingly being explored for structured data collection tasks as well. With a simple prompt, the Gemini Deep Research function can collect the requested data from publicly available sources. However, not all models can reliably complete such tasks; in fact,
Interview metadata often arrives in semi-structured formats, making it difficult to analyse or integrate into standard research workflows. This short prompt-based approach shows how GenAI can transform a JSON file containing interview-related metadata into a clean, structured Excel (.xlsx) file—in seconds, without requiring any programming knowledge. The method enables
Natural Language Processing (NLP) allows us to automatically analyse and categorise textual data. With the help of GenAI, even complex classification tasks can now be executed using just a well-designed prompt. In this post, we present a practical example where GenAI classifies financial-related sentences into domain-specific categories, showcasing how effective
Clustering refers to the unsupervised process of grouping similar entities, such as sentences, into categories based on shared characteristics without relying on predefined classes, thus revealing latent patterns in the data. For researchers, GenAI models enable this analysis without the need for programming expertise, offering a powerful means to explore
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
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
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
