Tag: prompt-engineering
7 items with this tag.
AI-powered practice growth
A full-day workshop for the International Private Physiotherapy Association's business meeting in Hamburg, run as four linked sessions: how AI actually works and how to prompt it well, building content for a real client group, getting that content found and trusted, and drafting a three-year business plan. Delegates build a single practice file across the day, using their own practice as the input, and leave with a persona, a piece of content in five formats, a communication plan, and a draft growth plan. Every exercise runs on free-tier tools.
Introduction to AI with Copilot
An internal staff development session for the CPC team introducing AI through Microsoft Copilot. Covers what AI is and isn't, safe working practices, structured prompting with the RGID heuristic, and hands-on practice — with the goal of each participant leaving with one specific task to try that week.
Prompt injection
Prompt injection is a technique in which instructions embedded in text cause an AI system to follow them as commands. In educational contexts it has appeared as an AI detection mechanism in assessment — which raises sharper questions about authorisation and trust than it might initially seem.
System prompt
Persistent context included in every message to an AI model, establishing consistent behaviour, knowledge, or constraints across interactions.
Prompt engineering
Using natural language to produce desired responses from large language models through iterative refinement
AI and the business of practice
An invited presentation at the Lincolnshire LMC Getting It Done Conference exploring how generative AI functions as a virtual business consultant — supporting practice management, strategic planning, and operational efficiency for healthcare practices. The talk covers six use cases: developing online presence, targeting patient groups, enhancing patient experience, strategic service development, ethical implementation, and change management.
The learning alignment problem: AI and the loss of control in higher education
Higher education's focus on prompt engineering — teaching technical skills for crafting AI queries — represents a misunderstanding of learning. This essay argues that prompts emerge from personal meaning-making frameworks, not technical mechanics, and that the institutional impulse to control AI interaction reveals a 'learning alignment problem': systems optimising for measurable proxies like grades rather than authentic curiosity. Drawing parallels to AI safety's value alignment problem, it shows how AI exposes that many assignments were already completable without genuine intellectual work. Universities must shift from control to cultivation paradigms, recognising that learning is personal and resistant to external specification, ensuring AI becomes a partner in human flourishing rather than a tool for strategic performance.