In this example, I’ll break down the complex prompt provided into a more Least-To-Most Prompting method. This L2M style demonstrates how to gradually increase complexity in prompts, leading to richer, more specific responses, while keeping the process systematic and intentional.
Key Characteristics of L2M Prompting:
- Incremental Complexity: Starts with a minimal prompt and gradually adds more detail or constraints in each step.
- Iterative Process: Each prompt builds on the response from the previous, refining the information sought.
- Scope Control: Focuses on asking broader questions initially, followed by specific details.
- Structured Workflow: Designed as a sequential approach, leading to a more comprehensive answer.
- Progressive Focus: Refines and narrows down the response as each prompt step addresses a new aspect or layer.
Key Benefits of L2M Prompting:
- Reduces Cognitive Load: Minimizes initial complexity, making it easier for the model (and the user) to process responses.
- Ensures Completeness: Encourages thorough exploration of the topic by covering all aspects progressively.
- Promotes Clarity and Precision: Stepwise refinement reduces ambiguity, leading to more accurate and targeted responses.
- Enhances Creative Problem Solving: Allows the model to explore a broad perspective before focusing, fostering diverse solutions.
- Minimizes Oversights: By examining each layer independently, it reduces the likelihood of missing important details.
- Efficient for Iterative Refinement: Ideal for situations where feedback loops are essential, enabling smoother refinements based on initial answers.
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
Here is a step-by-step breakdown of how you can rework the provided prompt into a Least-to-Most (L2M) prompting style prompt for learning and training the AI.
Step 1: Start with the most minimal prompt. The goal here is to extract very basic or surface-level information to initiate the conversation without overwhelming the model.
Minimal prompt:
“What are some ways to prevent website errors?”
or
“What is website loading latency?”
This prompt is simple and open-ended, aiming for general insights on error prevention without specifying the type of errors or any specific context.
Step 2: Add slightly more detail. Now, build on the initial response by narrowing the focus, specifying the type of errors you are interested in.
Slightly refined prompt:
“How can I prevent errors related to website loading latency?”
or
“How can website loading latency negatively impact user experience?”
This adds specificity, focusing on a common issue, website loading latency, but still doesn’t go into error messaging.
Step 3: Introduce an additional layer of complexity. Here, you add more details by specifying that you want the users to clearly understand any errors that may arise and why they are happening.
More refined prompt:
“How can I prevent errors related to website loading latency and make sure users understand the error and why it is happening?”
or
“What are some techniques to minimize website loading latency, and for each technique, describe how it helps users understand the error and why it is happening?”
By adding the context of user understanding, you steer the model toward suggesting solutions that involve better communication and clarity in error messages.
Step 4: Move to the most complex version. Finally, you incorporate the full details, including the number of suggestions (4), requesting examples from other successful designs, and making the goal of user satisfaction explicit.
Most refined prompt:
“I am designing a website and want to ensure a seamless user experience. Can you suggest 4 error prevention techniques to address website loading latency, while also making sure users understand 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.”
This is the full version of the original prompt, asking for four specific techniques, their successful implementations, and how they can lead to improved user satisfaction.
L2M Prompting Style Summary
Summary of Steps:
- Minimal: “What are some ways to prevent website errors?”
- Slightly refined: “How can I prevent errors related to website loading latency?”
- More refined: “How can I prevent errors related to website loading latency and make sure users understand the error and why it is happening?”
- Most refined: “I am designing a website and want to ensure a seamless user experience. Can you suggest 4 error prevention techniques to address website loading latency, while also making sure users understand 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.”
This step-by-step approach not only helps the AI understand and respond to the complex prompt effectively but also serves as a clear illustration of the Least-to-Most prompting style. This L2M style demonstrates how to gradually increase complexity in prompts, leading to richer, more specific responses, while keeping the process systematic and intentional.
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.
