AI Prompting Style Series: Self-Refine Prompting

In this example, I’ll break down the complex prompt provided into a more Self-Refine Prompting method. Self-Refine Prompting Style involves prompting the AI to generate an initial response, critically evaluate it, and then improve or refine the answer based on specific criteria. The workflow typically follows a cycle where the AI reviews its own output for weaknesses or areas for improvement and iterates on it.

Key Characteristics of Self-Refine Prompting: 

• Iterative Process: It involves a cyclical process of prompting, analyzing the output, and refining the prompt based on the AI’s response. This cycle continues until the desired outcome is achieved. 
• Active User Involvement: It requires active participation from the user to guide the AI’s responses. The user needs to critically evaluate each response and strategically adjust the prompt to steer the conversation. 
• Evolution of the Prompt: The prompt itself evolves and becomes more specific and targeted with each iteration. This gradual refinement helps to narrow down the scope and improve the accuracy of the AI’s output. 
• Feedback-Driven Refinement: The AI’s responses provide feedback that informs the subsequent refinement of the prompt. This creates a dynamic interaction where both the user and the AI contribute to the final outcome. 
• Exploration and Discovery: It encourages exploration of different facets of a topic by allowing the user to delve deeper into specific areas of interest based on the AI’s responses. 

Key Benefits of Self-Refine Prompting: 

• Enhanced Control: Provides greater control over the AI’s output by allowing the user to actively shape the conversation and guide the AI towards the desired information. 
• Improved Accuracy: Leads to more accurate and relevant results as the prompt becomes increasingly refined and targeted. 
• Deeper Understanding: Facilitates a deeper understanding of the topic by encouraging exploration and iterative learning. 
• Increased Flexibility: Offers flexibility to adapt the prompt based on the AI’s responses, allowing for unexpected insights and discoveries. 
• Tailored Output: Enables the generation of highly tailored and specific output that precisely meets the user’s needs. 

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 Overview

Step 1: Initial Prompt 

Start with the base question and allow the AI to generate an initial response.

Initial 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.” 

Step 2: Self-Evaluation Prompt 

Ask the AI to critically evaluate the initial response, identifying areas for improvement or missed elements in relation to the goal.

Self-Evaluation Prompt: “Now that you’ve provided 4 error prevention techniques, please review your response and identify any areas where the suggestions could be more specific, clearer, or better aligned with real-world use cases. What could be improved in terms of preventing loading latency errors and helping users understand the error?” 

Step 3: Refinement Prompt 

Instruct the AI to revise its initial output based on its own critique, improving clarity, precision, and alignment with the problem.

Refinement Prompt: “Based on your self-assessment, refine your previous response to include more actionable, detailed steps for preventing latency-related errors. Also, improve how the techniques can be communicated to users to make them better understand why an error occurs. Provide examples where necessary.” 

Step 4: Final Review and Example Request Prompt 

Have the AI conduct a final review and generate an optimized output based on the refinements, ensuring it includes successful implementation examples.

Final Review Prompt: “Take the refined suggestions and conduct one final review. Ensure the examples provided are highly relevant to real-world web design implementations and focus on improving user satisfaction. Make sure the error prevention techniques address both website latency and error communication effectively.” 

Initial Response (Step 1): 

1. Lazy Loading: Use lazy loading to prevent slow initial loading times. By loading only essential resources first and delaying non-critical ones, users can interact with the website while other content loads in the background. 

• Example: This technique is used by Pinterest to load images gradually as users scroll. 

2. Loading Indicators: Provide loading spinners or progress bars to let users know the site is processing their request. This manages user expectations and prevents frustration during high latency. 

• Example: Google Maps employs this when loading maps in low bandwidth situations. 

3. Error Messages with Context: When errors occur due to loading failures, display clear and informative error messages that explain why the error happened, e.g., “Network connection lost. Please check your internet connection.” 

• Example: Netflix shows specific error codes when it fails to load content due to connection issues, improving user clarity. 

4. Content Caching: Use caching mechanisms to store frequently accessed data. This reduces load times for returning users and ensures quicker page responses even under slow connections. 

• Example: YouTube caches video previews for faster reload times when users return to the homepage. 

Self-Evaluation (Step 2): 

• The techniques are useful, but the response could include more detail on how to implement these solutions specifically to reduce latency issues. 
• The examples, while relevant, lack depth on how they improve user satisfaction beyond basic functionality. 
• Error messages could be more tailored to the website’s specific context. 
• There could be more guidance on the technical implementation of caching and lazy loading to make it actionable. 

Refined Response (Step 3): 

1. Lazy Loading of Critical Content: Implement lazy loading with a focus on deferring non-essential scripts, images, and resources. Ensure that above-the-fold content (the visible part of the webpage when it first loads) is prioritized for immediate loading. 

• Improved Example: Instagram loads initial posts and images first, while deferring advertisements and other media, ensuring fast user interaction with the most relevant content. 

2. Interactive Loading States: Incorporate more interactive loading states, such as skeleton screens that give users a visual placeholder for content that’s still loading. This makes the wait time feel shorter and keeps users engaged. 

• Improved Example: Medium uses skeleton screens to load article previews, giving users a visual cue that content is coming while managing expectations about loading times. 

3. Contextual and Actionable Error Messages: Provide users with error messages that not only explain the error but also offer solutions. For instance, instead of just saying “Network connection lost,” add, “Try refreshing the page or check your Wi-Fi connection.” 

• Improved Example: Dropbox offers step-by-step troubleshooting within their error messages, guiding users to fix problems independently. 

4. Advanced Caching with Service Workers: Implement service workers to cache both static and dynamic content. This allows users to load the site even when they have a weak connection or are temporarily offline, drastically improving perceived load times. 

• Improved Example: Twitter Lite uses service workers to cache content, ensuring faster load times even on slow connections and offline access to previously viewed tweets. 

Final Review (Step 4): 

• The revised suggestions are now much clearer and focus more on actionable insights. 
• The examples are tied more directly to user satisfaction, showing how these implementations make the web experience smoother and reduce user frustration. 
• The refinements ensure the techniques are both practical and easily implementable for addressing latency issues while maintaining clear communication with users. 

Step 1: Initial Prompt 

Begin with a broad, open-ended prompt to gather initial ideas and possibilities: 

“I’m designing a website and want to provide a smooth user experience. What are some ways to prevent errors, especially those related to website loading times?” 

Step 2: Self-Evaluation

Analyze the response. Identify areas that need further exploration or clarification. For example, you might notice the AI hasn’t specifically addressed how to explain errors to users. Refine your prompt based on this reflection: 

“That’s helpful, but I also want to make sure users understand why an error is happening. Can you provide techniques that offer clear explanations to users about loading errors?” 

Step 2a: Deepen and Direct 

Review the new information. You might realize you need concrete examples to better understand the suggestions. Direct the AI to provide more specific information: 

“These explanations are good. Can you give me some real-world examples of how websites have successfully used these techniques to improve user satisfaction?” 

Step 3: Refinement Prompt 

At this stage, you might have a lot of information, but it might not be organized or prioritized. Focus the AI on the most important aspects and filter the information: 

“Of all these examples, which 4 techniques are most effective for preventing errors related to website loading latency and providing clear explanations to users? Can you rank them in order of importance?” 

Step 4: Final Review

Finally, ensure the AI provides a concise and actionable summary of the refined information: 

“Based on these refined suggestions, provide a final list of 4 error prevention techniques with clear explanations and examples, focusing on website loading latency and user understanding.” 

Summary

Key Takeaways

• Iterative Process: Self-refine prompting is an iterative process of asking, analyzing, and refining. 
• Active Engagement: It requires active engagement from the user to guide the AI towards the desired outcome. 
• Increased Control: This style gives the learner more control over the output and allows for exploration of different aspects of a topic. 
• Improved Understanding: By breaking down the prompt into smaller steps, learners can gain a deeper understanding of the subject matter. 

This step-by-step breakdown of self-refine prompting, using this specific example, will help you understand and apply this powerful technique in your own AI interactions and training. Try it out and have fun teaching you AI tool!

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.