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Student Resources: Generative AI: Prompt Engineering

Prompt engineering banner

What is Prompt Engineering?

Prompt engineering is an important part of using GenAI. In order to maximize our use of these tools, we want to input our questions or requests (referred to as a prompt) in the best way possible (creating or engineering the prompt) to get the best responses. I gave ChatGPT the following prompt: Provide a few sentences to explain prompt engineering for generative AI to a novice. CHatGPT gave me the following response:

“Prompt engineering is like giving specific instructions to a smart computer program. Instead of just asking a question, you carefully design the way you ask it to get the best results. It's about fine-tuning how you communicate with the computer, making your input more precise and tailored, so you can get the information or output you want. Think of it as crafting the right question or request to help the computer understand and respond in the most helpful way.”

Why is Prompt Engineering Important?Person talking to a chatbot

  • Maximize the power and ability of GenAI tools.
  • Work as efficiently and effectively as we can.
  • Think critically about what information we need and the best prompt to get that information.

Prompt Engineering Models

There are several models, formulas, frameworks, and suggestions for how to craft prompts. Many of these models are very similar and provide great guidance for prompt engineering.  It is important to note that these models haven't been scientifically tested, meaning we can't point to one and say that it has been proven to be more effective for writing prompts.  Instead, we should think of these models as guidelines. Be thoughtful and strategic about how you write prompts. Learn from each interaction about how GenAI tools work and apply that understanding to your next prompt. 

With that said, here is a simple model that can help you to craft effective prompts. The CAST model was developed by developed by Heidi Jacobs and Michael Fisher of Association For Supervision of Curriculum and Development. It contains 4 elements: Criteria, Audience, Specifications, and Testing.  Usually, it is best to include all of these elements, but not every request or input may require that.  Think critically and use your best judgement. Part of working with GenAI is trial and error. 

The Cast Model

  • Criteria
  • Audience
  • Specifications
  • Testing

The CAST Model for Prompt Engineering

When writing your prompt, first think about the output you want to receive. The parameters that you set for the output is the criteria. Parameters may include the output format, length, or other details. Graphic of robot checking off a list on a clipboard

Examples of Criteria:

  1. What do you want your output format to look like? A bulleted list? A paragraph? Short sentences? 
     
  2. What type of response do you want? A summary? Examples? Step-by-step instructions?

 

 

Your prompt should indicate who the output is for (the audience) and what their level of understanding of the topic is. This will make the output more relevant and tailored to the user. In some cases the audience may be you, but in other cases you may want AI to generate information for other people. Depending on the topic, the intended audience may have a different level of understanding or background knowledge than you, so it is important to specify this within the prompt.Graphic of a robot looking a smartphone

Examples of Audience:

  1. You (specify your level of understanding)
     
  2. Someone else with limited information on the topic
     
  3. A professor with a doctorate degree in chemistry.

Prompts should include as many relevant details as possible along with specific examples if needed.  Generally speaking, the more detail you provide, the more relevant the output will be. Include specific examples of topics that you want the output to address or include if that is important to your request.

Prompt Examples with SpecificationsGraphic of a robot with construction tools

  1. Generate a list of 5 research topics for a 5-7 page paper for a first year undergraduate psychology class.
  2. Help me prepare for a first year college anatomy exam. Provide 10 short answer questions that focus on the topics of homeostasis, organic compounds for human functioning, and cell structures.

Although we may follow the guidelines for effective prompt writing, sometimes we don't get the result that we need or expect.  However, we can learn from each response to help us improve our prompts for next time. Each interaction with Gen AI can help us better understand how the tool works and how we can best structure our prompts to get the best result.  Testing includes analyzing, revising, and reflecting.Graphic of a robot putting a puzzle together

Examples of Testing:

  1. Analyze: Does the output meet my needs?
     
  2. Revise: What should I add or edit from my original prompt? OR What follow up prompt should I try?
     
  3. Reflect: What did I learn about how AI works that I can apply to my next prompt? 

Use Prompt Engineering to Enhance Your Job Applications Materials

Looking to level up your job applications and interview preparation? Enroll in the self-paced Canvas course: Next Level Job Applications and learn how to use Gen AI to enhance your resume and promote your best self to employers. 

Academic Misconduct Disclaimer!

Generative AI tools can be helpful and fun, however they may be prohibited for use in your courses.

Each instructor and assignment may have different requirements and permissions regarding the use of  AI tools. It is crucial to check with your instructor about their specific policy before using these tools for your coursework. 

If your instructor has permitted the use of AI, be sure to cite it as a source when paraphrasing, summarizing, or quoting text or using images.  Always check with your instructor if you are unsure of how to cite AI. Here is an example of how to cite AI.

Example: Citing ChatGPT and other generative AI.

Failure to cite AI or disclose the use of AI when prohibited may be considered cheating, academic dishonesty, or plagiarism and may result in a failing grade or another consequence as outlined in section 2.3.11 of the Maricopa Community College District’s Administrative Regulations.