Enhancing AI Prompt Composition in 4 Steps
In the ever-evolving world of education, the importance of writing effective AI prompts is becoming increasingly significant, particularly in aiding time management and mentoring students in AI usage. Graham Clay, an AI-in-education expert and author of the AutomatED newsletter, emphasizes the importance of assuming that it's the prompt that needs work to improve AI prompt writing skills.
To write AI prompts effectively for education, it's beneficial to employ structured frameworks such as the CRE Framework. This framework, an acronym for Context, Request, and Example, offers a clear and concise structure for crafting prompts.
- Context provides background or relevant information to guide the AI’s understanding.
- Request clearly specifies what you want the AI to do (e.g., summarize, analyze, generate).
- Example shows the AI the desired format or style of the response.
This structure helps reduce ambiguity and improves prompt clarity, enhancing output quality.
To experiment effectively with the CRE Framework, start with a clear objective or question. Break down complex prompts into smaller parts for better results. Try multiple versions by varying keywords, including or excluding concepts, and adjusting tone or detail. Use feedback from the AI’s responses to refine and adapt your prompts iteratively.
Learning from others involves reviewing prompt libraries, templates, or community-shared examples to understand effective strategies and phrasing. For instance, GitHub repositories like the Veo 3 Prompting Guide offer detailed real-world prompt templates with context, audio, and camera movement cues that can inspire educational prompt designs.
In summary, effective AI prompts in education leverage: - Clear, specific language and structured frameworks (e.g., CRE) - Iterative experimentation and adaptive refinement - Examining and adapting effective prompt examples from others
This approach promotes better AI-generated learning materials, critical thinking, and student engagement. The CRE Framework is a strategy learned from a free course focused on using ChatGPT for education, developed in partnership between Common Sense Media and OpenAI.
Embracing the iterative part of the prompt writing process has helped the author get more out of AI for specific tasks and better understand the technology. Although there have been experiences where the AI's output was not impressive, a better understanding of AI prompts can help teachers give a better sense of how students might use AI to cheat.
The AI For Education website provides prompt libraries based upon common education needs, including fundraising prompts and ones built around Bloom's Taxonomy. The Common Sense Media course offers a library of AI prompts that can be customized to unique situations and needs.
If a prompt doesn't work at first, it's necessary to adjust it and tweak the inputs. This may involve asking the AI for more examples, trying again while avoiding something not wanted, or searching for existing prompt templates. The philosophy of assuming that the AI prompt needs work has led to better understanding and improvement in AI prompt writing skills.
- The CRE Framework, a structured approach for crafting effective AI prompts in education, includes clear context, specific requests, and examples, helping to reduce ambiguity and improve prompt clarity.
- Examining and adapting effective prompt examples from others, such as those found in libraries or community-shared resources, can promote better AI-generated learning materials and student engagement.
- Effective AI prompts in education can lead to better AI-generated learning materials, critical thinking, and student engagement, as proven by the author's experience with using the iterative process of refining AI prompts.
- Online resources like the AI For Education website offer prompt libraries based on common education needs, including fundraising prompts and ones built around Bloom's Taxonomy, to help teachers and students with their learning and self-development.
- Teachers who practice the philosophy of assuming that the AI prompt needs work can develop a better understanding of AI prompts, which may help them understand how students might use AI to inappropriately cheat in their academic work.
