Applying prompts to GenAI/LLMs

A proof of concept argument for student-centred module leaders A tweet recently caught my attention  https://x.com/ihtesham2005/status/2041576806810370553?s=20 . It described an MIT student who had developed what he called “context stacking” — uploading lecture materials, readings and related papers into an AI tool before each class, then using carefully constructed prompts to build a mental model of the content before setting foot in the lecture hall. By the time he arrived, the professor wasn’t teaching him anything new. They were confirming, refining and occasionally surprising him. That surprise, he said, was the only thing he wrote down. This is not simply pre-reading with extra steps. Using generative AI as an external thinking partner, this student was identifying gaps in his own understanding before the lecture began — doing what good tutors have always done, asking not “what do you know?” but “where does your understanding break down?” This maps directly onto the highe...

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...

For many professionals, one of the hardest parts of any project isn’t the work itself—it’s the "blank page" problem. Whether you are drafting a project proposal, conducting a literature review , or  brainstorming  innovative solutions, getting started can be a significant mental hurdle. Generative AI (GenAI) tools like ChatGPT, Claude, and Copilot are often described as "content creators," but their true value for professionals lies in enhancing productivity. By applying specific prompting techniques, you can transform a vague idea into a well-structured, reference-supported document in a fraction of the time. Here is a three-step workflow to turn GenAI into your ultimate research and planning partner. 1. Brainstorming with "Personas" and "Templates" Don't just ask the AI for "ideas." To get professional results, you must give the AI a Persona (who it is acting as) and a Template (how you want the output to look). In our recent ex...

From Google Scholar to Project Ideas or Using AI to Map the Future of Your Research Generated as well by Google Gemini Have you ever looked at a researcher’s Google Scholar profile and felt overwhelmed? Between the long lists of citations and dense technical titles, "connecting the dots" of a decade-long career is a massive cognitive lift. Whether you are a student hunting for a dissertation topic or a professional scouting for a collaborator, understanding the trajectory of research is harder than reading the papers themselves. In my latest experiment, I used Google Gemini to see if it could bridge this gap. I gave it a specific challenge: Analyze my own research profile and design 10 compelling project ideas for a final-year Computer Science student. The Prompt: Turning Data into Direction The secret to a good AI output is giving it a clear "anchor." By providing a URL to a live dataset (my Scholar profile), I bypassed the need to copy-paste thousands of words....

Well sort of! There are approaches where we can get the generative AI to look at a problem from multiple perspectives (or personas) and bring the ideas generated, ideally informed by the others. to a final plan. One of the main strategy is called Tree of Thoughts (see here for more detail  https://www.forbes.com/sites/lanceeliot/2023/09/08/prompt-engineering-embraces-tree-of-thoughts-as-latest-new-technique-to-solve-generative-ai-toughest-problems/?sh=5ce79bdb2c8b ). The central idea is get a number of expert opinions, allow potential cross-fertilization of ideas, come up with actions or plans. Let see this action.  Scenario: Find out about the UK Government's plans on Disability support and then use Tree of Thoughts to produce some ideas for a company making disability equipment based on their website. Google's Gemini will be used. Stage 1 "UK Governments plans on Disability support ": Prompt:  Read, convert to plain text and consolidate information from the followi...

This is a short companion post to the previous post about creating ideas for blog posts. In this post using ChatGPT4 the ideas suggested previously are used to create images for the five blog posts. ChatGPT4 allows access to DALL-E so  it takes in the title, summary and outline from the previous post and then using the prompt " For each of the ideas create an image that summarises it ." it produced the following images. You can follow up then and ask it save them and it produces clickable download links to them. One further trick is to ask ChatGPT to save them all together as a zipped file. This ability of later ChatGPT to create and read zip files is incredibly useful. Please feel free to add comments on what you have done with these or other interesting things to do.