Tag: prompt-engineering
10 items with this tag.
Practical AI for private practice
A four-hour online masterclass for rehabilitation clinicians in the Kinatex network, run as four hands-on sessions on patient education, professional development, admin and business, and closing with patients who bring their own AI into the clinic.
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.
Harness engineering
Harness engineering is the practice of building the full architectural scaffolding within which AI agents operate — structured documentation they can reason with, constraints that enforce invariants, and feedback loops that let them know when they've succeeded. It is distinct from prompt engineering, which shapes individual tasks, and from oversight, which monitors outputs after the fact. The harness is the infrastructure that makes delegation coherent at scale.
Prompt injection
Prompt injection is a technique in which instructions embedded in text cause an AI system to follow them as commands. In education it has appeared as a way of detecting AI use in assessment, which raises questions about authorisation and trust that the technique itself can't answer.
System prompt
Instructions placed in front of every conversation with an AI model, setting how it behaves, what it knows, and what it won't do, usually without the user seeing them.
Prompt engineering
Writing instructions for a language model in plain language, and refining them through conversation, until it produces what you need. The skill is closer to managing a colleague than to programming, which is why educators who supervise and give feedback already have most of it.
Context engineering
How context engineering differs from prompt engineering: building the knowledge and structure a language model needs to reason well about your work.
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.