In two recent blog posts, we demonstrated how scanned PDFs can be processed using Mistral’s tools—first via a prompt-based approach in Mistral Le Chat, and then through the Mistral OCR API. Both methods effectively extracted readable, structured text from complex documents. But how well do other generative AI
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
Except for Claude 3.7 Sonnet, GPT-4o and GPT-4.5 (which we discussed in a separate post), all other GenAI models we tested encountered issues when asked to generate a simple bar chart from a CSV file. The limitations varied widely—from difficulties in reading the file and missing visualisation
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,
