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...

Registration page for a new pet When using Large Language Models to convert specifications into code, the boundaries of your input heavily shape the boundaries of the model's output. If you feed an LLM an API specification, its baseline assumption is that you want an API. This short case study explores what happened when converting a standard OpenAPI 3.0 specification into a runnable Flask project using Claude.ai—and how adding a single sentence to the prompt dramatically altered the architectural and visual output. The Experiment & The Starting Prompt The goal was simple: take the standard OpenAPI 3.0 Petstore example specification and prompt Claude.ai to turn it into a complete, runnable Flask application pre-packaged for Visual Studio Code. Here was the initial prompt structure: Plaintext In the next prompt I am going to enter an OpenAPI specification. Your role is to convert this into a Flask-based program and then save the files as a zipped-up file ready for importing i...

AI in Education Prompt engineering, context engineering and agentic AI are often used interchangeably, but the literature treats them as distinct. Prompt engineering concerns crafting effective single instructions to a model (Glean, 2026). Context engineering is the broader discipline of designing and managing the entire informational environment around a model—memory, retrieval, tool outputs and conversation state—rather than a one-off instruction (Abstracta, 2026); prompt engineering operates within the context window, while context engineering determines what fills it (arXiv:2606.12422, 2026). Agentic AI describes systems that plan and execute multi-step tasks with delegated autonomy, raising organisational questions of accountability rather than purely technical ones (MIT Sloan, 2026; Palo Alto Networks, 2026). There is genuine debate about prompt engineering's durability. IEEE Spectrum (2025) reported research suggesting prompting is increasingly performed by models themse...

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...