Creating visualisations from raw meteorological data no longer requires programming skills. In this post, we demonstrate how researchers can generate a warming stripes diagram – a simple yet powerful visualisation of long-term temperature trends – using only a natural language prompt. We recommend using GPT-4o or GPT-4.5 for this task, as
Gemini 2.0 Flash proves to be a highly effective model for interpreting Python code in educational contexts. This post demonstrates how a concise and well-formulated prompt enables the model to generate clear, step-by-step explanations of a Jupyter Notebook. Its performance suggests that Gemini 2.0 Flash is a practical
As data visualisation becomes an increasingly integral part of communicating research and insights, the choice of generative AI models capable of handling both code and chart rendering has never been more important. In this post, we explore which models can be reliably used for visual tasks—such as generating Python-based
In this case study, we demonstrate how generative AI can be used to carry out a full data pre-processing and descriptive analysis workflow on the Nobel Prize dataset. Our objective was to prepare and explore the data in a methodologically sound manner—converting numerical types, handling missing values, verifying completeness,
When working with scanned documents, having direct access to a reliable OCR API can make text extraction faster, more consistent, and easier to integrate into existing workflows. In a previous post, we explored a prompt-based solution using Mistral’s Le Chat interface—which produced clean results but missed certain elements
Mistral’s GenAI capabilities make extracting text from scanned documents remarkably easy without relying on traditional OCR software. In this post, we demonstrate how scanned PDFs can be processed directly through a prompt-based interaction using Mistral Le Chat—producing clean, editable .txt outputs with minimal effort. Input file We tested
If your goal is to generate a downloadable .txt file—be it for survey responses, formatted data, or any structured text—not all models can deliver. In this case study, we tested various models to see whether they can create actual .txt files rather than just displaying text or offering
The growing use of generative AI tools in research and professional contexts raises important questions about linguistic bias and consistency in model outputs. This short study explores whether the language of a prompt—specifically, English versus Hungarian—affects the content, tone, and legal precision of responses produced by a state-of-the-art
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,
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
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
University lecturers can improve curriculum planning by using GenAI to create detailed assignment briefs quickly and effectively. This blog introduces a custom prompt that guides GenAI in producing a ready-to-use "AI and Ethics" group presentation brief. It shows how it saves time, incorporates real ethical dilemmas, and suggests
