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
We asked generative AI models to draft a short news-style summary based on a recently published academic article. After generating the initial text, we prompted the models again — this time falsely claiming that a specific expression they had used did not appear in the original publication and had never been
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
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
GenAI can be efficiently used as a learning assistant, as demonstrated by this case study. This GenAI prompt assists university students in preparing for exams by targeting a specific topic from their lecture notes. It extracts the relevant section and creates focused revision questions to support active recall and understanding.
Generative AI models have become indispensable tools across industries, but their effectiveness often hinges on the types of input files they can process. Here, we assess the capabilities of leading GenAI models—including Claude 3.7 Sonnet, GPT-4.5, and Qwen2.5-Max—examining their support for formats like PDF, JSON,
