Applying prompts to GenAI/LLMs

co-author map The National Teaching Repository (NTR) has just won a prestigious national award for teaching and learning — the Collaborative Award for Teaching Excellence 2026 . As a member of the NTR board (I am hiding in the bottom left corner of the team image :- ), and someone who spends a lot of time on this blog testing what gen AI is actually good for, I wanted to use the moment to try something on the repository itself: could AI help surface patterns in 300+ pieces of teaching scholarship that aren't visible just by browsing it? Two activities came out of this — mapping who has co-authored with whom, and then a thematic analysis of what the repository is actually about. One needed code. The other didn't. But the thing I actually want to talk about is a prompting habit that ran through both, because it's the part any educator can pick up and reuse, whether or not you can write a line of Python. All the prompting was done with Claude.ai. The pattern: ask before y...

I've been learning Python data visualisation, working through Murat Durmus's Hands-On Introduction to Essential Python Libraries and Frameworks alongside the official Dash tutorial . The resulting code was functional — a basic bar chart comparing data for San Francisco and Montréal — but it looked like exactly what it was: a beginner's first attempt. Plain white background, default colours, numbered axes, and a title that just said "Data Viz." So I decided to run an experiment. Could Claude AI turn a scrappy 20-line script into something genuinely worth showing people? Before running the prompt The First Prompt I pasted the code into Claude.ai with a simple instruction: "Rewrite this following code to be graphically more interesting." The result was striking. Claude switched to a dark "neon terminal" aesthetic — deep navy background, electric teal and magenta accents, and a stylish monospaced font. The bars got proper labels, the axe...