Applying prompts to GenAI/LLMs

New article crafted between myself and Claude.AI both been critical but constructive about each 'others' contribution, and refinement of the replies and editing after. The starting prompt is shown after the references Context Engineering Isn't the New Software Engineering — But Ignoring It Is Still a Mistake What context engineering actually is Why "the new software engineering" overstates it The more defensible version of the argument Where the evidence runs thin — and where curriculum reform gets hard Conclusions Context engineering is a real, evidenced, currently valuable skill — not hype invented from nothing. It sits downstream of genuine engineering problems (retrieval, state management, information curation) that graduates will encounter in real jobs. It is not a replacement for software engineering fundamentals, and framing it that way overstates the case and risks looking like marketing rather than analysis. The stronger, teachable claim is that context e...

AI tools often produce their weakest results when pushed for a finished answer too early. A far more effective pattern flips this approach: instead of demanding an immediate analysis of a file or webpage, the user designs a prompt that forces the conversation to slow down. This deliberate pause allows both user and AI to clarify aims, test assumptions, introduce alternative viewpoints, and refine the output through iterative questioning. The goal isn’t simply to generate a longer response, but to establish a process that makes the final outcome clearer, broader, more critical, and ultimately more useful. While this prompt serves as a purposely generic blueprint of the earlier, more specific examples shared on this blog, its core value lies in its structured model: Source Identification: The AI first establishes the nature of the document, whether a file or a URL, allowing greater flexibility in source. Iterative Dynamic: The system asks targeted questions—strictly one at a time—gat...

I recently found myself watching a recording of Jean-Michel Jarre’s 2025 concert in Seville. Alongside the music and spectacle, there was a lot of discussion around AI and AI-generated content. The concert can, at the time of writing, be found on  https://www.arte.tv/en / well worth a look. That set me thinking, not in a grand theoretical way at first, but in a simple, curious way: what would ChatGPT visualise if I asked it to turn Oxygène into a single cartoon image? Not “make a copy of the album cover”. Not “recreate Jean-Michel Jarre’s visual style”. Just: take the word, the title, the atmosphere it suggests, and see what happens. That distinction mattered to me. Oxygène already has a famous visual identity, and the point of the experiment was not to imitate it or produce a substitute. It was more about asking what an AI system does when given a culturally loaded musical reference, a single evocative word, and a loose creative brief. Would it lean towards the known album im...

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...

Same Prompt, Four AIs — Why the Answers Aren’t the Same The differences aren’t just in the answers—they’re in the thinking Generative AI tools are often discussed as if they were interchangeable—different interfaces delivering broadly similar outputs. However, when applied to complex intellectual tasks, meaningful differences begin to emerge. To explore this, I ran the same academically rigorous prompt through four leading systems—Claude, ChatGPT, Google Gemini, and Copilot. The task required a full thematic analysis of a researcher’s career using the framework developed by Virginia Braun and Victoria Clarke . What followed was not simply variation in output, but variation in how each system approached the act of analysis itself. Same Input, Different Interpretations At a high level, the experiment is simple: One prompt → Four models → Four distinct approaches What changes is not the instruction, but how each system: Interprets the task Handles uncertainty Applies methodology Defines ...