AI Prompting Techniques

Practical prompting frameworks that help you direct AI and judge its output.

How do you get from a blank chat box to work you can stand behind? A useful prompt begins with the work you need to do and the knowledge you bring to it. First, decide whether AI suits the task. If it does, state your goal and give the model the context it needs.

Our six-stage AI Prompting Framework guides you through the whole exchange. You review the first response and improve the work through conversation. The final stages ask whether the result reflects your own knowledge and how you will document the AI’s contribution.

Prompting can also support reasoning. A student can ask a language model to raise likely objections to an argument. The student then judges each objection and decides how the argument should change. The value lies in making those judgements, even when the AI’s suggestions are wrong.

Ballsun-Stanton, B., Torrington, J., & Waked, L. (2026, September 4). AI Prompting Framework [Poster]. https://doi.org/10.5281/ZENODO.22290781

Poster

Read abstract

The AI Prompting Framework is a structured, six-stage guide (numbered 0 to 5) for working with generative AI in a way that keeps the user's knowledge, expertise, and judgment at the centre of the process. Rather than treating AI as a source of ready-made answers, it positions the tool as a collaborator whose output the user directs, scrutinises, and ultimately owns.

The framework moves from an initial decision about whether AI is appropriate for the task at hand (Step 0), through the preparation of a clear, well-contextualised first input with a built-in self-check (Step 1), to reviewing that first response (Step 2), interacting iteratively while retaining control of the exchange (Step 3), reflecting on whether the final output genuinely reflects the user's own knowledge (Step 4), and finally documenting effective prompts and being transparent about AI use (Step 5). Each stage is paired with a reflective question, such as "Have I provided enough context?" and "Am I happy to put my name to this?", that prompts users to pause and exercise judgment throughout.

Designed to be general-purpose, the framework applies across disciplines and professional contexts wherever people want to use generative AI deliberately, critically, and accountably.

Ballsun-Stanton, B., & Torrington, J. (2025, October 17). Effective AI Prompting Strategies from the Humanities [Poster]. https://doi.org/10.5281/ZENODO.17373463

Poster

Read abstract

Poster to support successful prompting by teachers and researchers.

Good AI prompts are essential to predictably and consistently getting a desired output.There are no single phrases that work across all situations - AI will simply amplify the user’s capabilities, creativity, judgement and discernment.

These effective prompting strategies will help develop your own skills and strategies with AI prompting.

Text:

Start with a goal: what is the single thing you are trying to achieve?

Add context and details: requirements, instructions and parameters. AI needs clear scaffolding so it’s set up for success. Don’t ask it to search for facts - it’s not a search engine.

State your role, so it knows what register to use. The use of words influences its sample and response. Tell it what you want and who the output will be for.

Work as thinking partners: Tell the AI to “Ask me one critical question at a time until we achieve our goal”

Check for clarity: Include a final sentence for the AI to scaffold the task to make sure what you said is what you meant. “To begin, please functionally decompose the task.”

Use examples. AI is effective at imitation, and if you give it a few examples of what you want, it can copy their form.

Longer prompts, rather than shorter: Make sure that you provide all the details it needs. Edit your prompts if errors occur, or start over.

Clear expectations: At the end of each prompt, end with a single, specific task.

Have a real conversation: Go back and forth a few times before you reach your answer. Asking it to ask you for clarification highlights blank spots in your thinking.(Also, you can prompt it to help you design prompts…)

Avoid “AI-slop” - Would you be comfortable putting your name to this? Have you interacted using some effort? Make sure you know enough to be able to validate the output.

Kudina, O., Ballsun-Stanton, B., & Alfano, M. (2025, February 1). The use of large language models as scaffolds for proleptic reasoning. Asian Journal of Philosophy, 4(1), 24. https://doi.org/10.1007/s44204-025-00247-1

Paper

Read abstract

Abstract This paper examines the potential educational uses of chat-based large language models (LLMs), moving past initial hype and skepticism. Although LLM outputs often evoke fascination and resemble human writing, they are unpredictable and must be used with discernment. Several metaphors—like calculators, cars, and drunk tutors—highlight distinct models for student interactions with LLMs, which we explore in the paper. We suggest that LLMs hold a potential in students’ learning by fostering proleptic reasoning through scaffolding, i.e., presenting a technological accompaniment in anticipating and responding to potential objections to arguments. Here, the technical limitations of LLMs can be reframed as beneficial when fostering anticipatory reasoning. Whether their outputs are accurate or not, evaluating them stimulates learning. LLMs require students to critically engage, emphasizing analytical thinking over mere memorization. This interaction helps solidify knowledge. Additionally, we explore how engaging with LLMs can prepare students for constructive collective discussions and provide first steps in addressing epistemic injustices by highlighting potential research blind spots. Thus, while acknowledging the sociopolitical and ethical complexities of using LLMs in education, we suggest that when used in an informed way, they can promote critical thinking through anticipatory reasoning.