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...

B rief recap In an earlier post, “Same prompt, four AIs: why answers aren’t always the same” , I looked at what happened when the same prompt was given to four different LLMs. Unsurprisingly, perhaps, the answers were not identical. That raised an interesting follow-on question: what was the prompt actually trying to do? The answer is that it was trying to support a form of thematic analysis. In that case, the object of analysis was an academic profile, including my Google Scholar profile. The version I discuss here is a modified prompt, used with Claude.ai, where I uploaded a CV and asked the system to find other appropriate public resources connected with a named person and institution. The aim was not simply to summarise the CV, but to identify visible and less visible themes across a whole career. Why use thematic analysis? Thematic analysis is widely used by academics, especially in qualitative research. Braun and Clarke’s well-known paper, Using thematic analysis in psy...