In this example, I’ll break down the complex prompt provided into a more Maieutic Prompting method. Maieutic prompting, based on the Socratic method, involves leading the user to their own conclusions through guided questions that encourage reflection and discovery. The key here is to break down the problem into a series of insightful, open-ended questions, helping the user uncover deeper insights step by step. Here’s how you can rework the original complex prompt using the Maieutic style.
Key Characteristics of Maieutic Prompting:
- Open-ended questions: Prompts start broad and gradually narrow down the focus.
- Probing for deeper understanding: Questions encourage reflection and elaboration.
- Guiding towards solutions: Prompts subtly steer the individual towards potential answers without dictating them.
- Emphasis on self-discovery: The goal is to help the individual arrive at their own conclusions.
Benefits of Maieutic Prompting:
- Encourages active learning: The user is actively involved in the learning process.
- Promotes deeper understanding: The user is more likely to internalize the concepts.
- Fosters creativity and critical thinking: The user is challenged to think outside the box.
- Develops problem-solving skills: The user learns to identify and address challenges independently.
Original Prompt
“I am designing a website and want to ensure a seamless user experience. Can you suggest 4 error prevention techniques that I should integrate into my design? Specifically, I want to prevent errors related to website loading latency and ensure that users have a clear understanding of the error and why it is happening. Please provide examples of how these techniques have been successfully implemented in other designs to improve user satisfaction.”
Workflow
Now I am going to treat my AI model like I would any website designer and ask the questions accordingly as if it were a real person. You might be surprised at the responses that you get as you might even get a question in return. If so, see where it leads you, as it is always a good learning experience!!
Step 1: Initial Exploration
Begin by asking open-ended questions to the AI that prompt reflection on the general goals of the design.
Question Prompt:
“When designing for a seamless user experience, what elements do you believe are critical to avoiding frustration for your users?”
or
“Imagine you’re a user visiting a website for the first time. What are some initial frustrations you might experience that could lead to a negative impression?”
- These invite the AI to think holistically about the user experience and to frame error prevention as a key component of usability.
Step 2: Focus on Specific Problems
Guide the AI to narrow down the scope by reflecting on the specific issue of error prevention in relation to website performance.
Question Prompt:
“What are some common reasons websites experience loading issues, and how do you think this impact user satisfaction?”
or
“You mentioned frustration with slow loading times. Can you elaborate on why that’s a problem and how it makes you feel as a user?”
- This question encourages the AI model to consider technical reasons behind loading issues and how they affect the user, leading to potential solutions.
Step 3: Shifting to Error Prevention
Once the AI has considered the problem, lead them toward thinking about prevention strategies.
Question Prompt:
“Given the importance of website speed, what methods have you come across or used that could reduce loading latency errors in a way that improves user experience?”
or
“If you were designing the website, how might you address the issue of slow loading times to keep users engaged?”
- Here, you’re prompting the AI model to recall or research specific techniques they know of, bringing those insights to the surface.
Step 4: Guiding to Examples
Encourage the AI model to explore real-world examples of successful implementations of these techniques.
Question Prompt:
“Can you think of a website that you’ve encountered that handled errors, particularly related to loading times, in a way that was clear and helpful? What did they do, and how did that influence your experience as a user?”
or
“Think about a time when you encountered a website with a well-handled loading issue. What techniques did they use, and how did it impact your overall experience?”
- This invites the AI model to draw from their own experiences or known examples, helping them understand how to model their design based on successful practices.
Step 5: Encouraging Solution Development
Ask the AI Model to consolidate their reflections into actionable solutions.
Question Prompt:
“Based on what we’ve discussed, what are four strategies you could apply to your own design that would help prevent these errors and clearly communicate any issues to the user?”
or
“Based on our discussion, can you suggest 4 specific error prevention techniques related to website loading latency that would improve user satisfaction? Please provide examples of how these techniques have been successfully implemented in other designs.”
- This step allows the AI model to solidify their thoughts into concrete techniques and build their own solutions with clarity.
Step 6: Connecting Insights to Broader Concepts
Finally, encourage the AI model to think about how these strategies can fit within the broader framework of improving overall user satisfaction.
Question Prompt:
“How do you think implementing these techniques will impact the overall user experience, and in what ways might it enhance user satisfaction?”
or
“Do you think these techniques will help with the overall user experience? How do you think it would do that specifically and how can this enhance user satisfaction for future experiences?”
- This leads the AI model to consider the end result, helping them connect error prevention with a broader goal of user happiness and trust.
Summary
The Maieutic process reworks the original prompt into a guided series of questions, gradually leading the learner from a high-level understanding of error prevention toward specific, actionable insights. The end result is that the learner will not only arrive at four error prevention techniques but will have critically reflected on why these techniques are useful, how they have been applied before, and how they can apply them in their own design process. Thus, making their own decisions from what the AI model has given them in their conversation.
This method emphasizes the learning process, ensuring that the learner fully understands the “why” behind the “what,” rather than simply receiving a set of suggestions. So have fun with it learn something new while training your AI model!
About Lance Lingerfelt
Lance Lingerfelt is an M365 Specialist and Evangelist with over 20 years of experience in the Information Technology field. Having worked in enterprise environments to small businesses, he is able to adapt and provide the best IT Training and Consultation possible. With a focus on AI, the M365 Stack, and Healthcare, he continues to give back to the community with training, public speaking events, and this blog.
