In this example, I’ll break down the complex prompt provided into a more Contrastive Prompting method. Contrastive prompting focuses on explicitly highlighting the desired aspects of a response by presenting them in contrast to less desirable aspects. This involves providing examples of both good and bad outputs, or by framing the prompt in terms of what to do and what not to do. This style encourages the AI to generate output by comparing and contrasting factors, thus highlighting differences and similarities that enhance understanding.
Use Cases:
This style is particularly effective when:
- You want to fine-tune the AI’s understanding of nuanced concepts.
- You need to steer the AI away from common pitfalls or biases.
- You are looking for highly specific types of responses.
Key Characteristics:
- Comparison Focused: Prompts ask for differences or similarities between two or more aspects. Includes both positive and negative examples. Often uses comparative language (“do this, not that”).
- Structured Insights: Clearly distinguishes between desired and undesired output characteristics. Responses highlight distinctions or similarities systematically, often leading to clearer, more structured insights.
- Nuanced Detail: Allows for deep exploration of the pros, cons, or effectiveness of various options.
Key Benefits:
- Enhanced Clarity: By drawing out contrasts, this style often clarifies complex subjects. Reduces ambiguity and increases the precision of the response.
- Decision-Making Aid: Offers detailed comparisons that support informed decision-making. Helps the AI learn nuanced distinctions and avoid common errors.
- Improved Insight: Encourages deeper understanding of the strengths and weaknesses of each contrasting element. Provides a clear framework for evaluating the quality of the output.
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 Using Contrastive Prompting Style
Thought Process: Think of how you would rework the topic in your head and get an idea of what you want to accomplish with the prompting task
Step 1: Identify the Core Request: In this case, the core request is to suggest error prevention techniques for website loading latency.
Step 2: Define Desired and Undesired Outcomes:
- Desired: Clear explanations of errors, user-friendly guidance, proactive prevention measures.
- Undesired: Technical jargon, vague error messages, reactive solutions.
Step 3: Develop Contrasting Examples:
- Good Example: “Instead of simply displaying ‘Error 404’, provide a message like ‘Oops! The page you’re looking for seems to be lost. Try searching or go back to the homepage.'”
- Bad Example: “Display a loading spinner indefinitely without any indication of progress or potential issues.”
Step 4: Integrate Examples into the Prompt: Rephrase the prompt to incorporate these contrasting examples.
Step 5: Have a Comparison Summary of the Topic: Bring it all together go gain the proper insights on the topic.
Reworked Prompt Chain (Contrastive Style):
Start with a Comparative Introduction:
“When designing a website, ensuring a seamless user experience often involves strategic error prevention techniques. Compare and contrast two sets of error prevention methods: one focusing on real-time error notifications and another focusing on proactive design adjustments that reduce the chances of latency issues.”Define Specific Error Types to Contrast:
“Identify two approaches to mitigate loading latency errors: one involving user notifications that clarify loading stages (e.g., progress bars) and another that emphasizes architectural optimizations (e.g., lazy loading). Discuss how each approach impacts user understanding and satisfaction.”
Request Detailed Contrasts on User Awareness:
“Contrast how each approach provides error clarity to users. For example, how does a notification of loading progress vs. a backend optimization impact a user’s understanding of why an error may have occurred?”Ask for Comparative Real-World Examples:
“Provide examples of websites where each technique has been implemented. Contrast how these websites handle loading latency errors and evaluate the impact on user satisfaction in each case.”Conclude with a Comparison Summary of Effectiveness:
“Summarize the effectiveness of these two contrasting methods in reducing loading latency errors and improving user experience. Highlight which approach is more effective in enhancing user satisfaction and why.”
Single-Shot Reworked Prompt (Contrastive Style):
“I’m designing a website and want to prevent errors related to loading latency. My goal is to provide a seamless user experience even when things go wrong.
Do this:
- Offer clear, concise explanations of the error in plain language. For example, instead of just saying “Error 404”, say something like “Oops! The page you’re looking for seems to be lost. Try searching or go back to the homepage.”
- Proactively prevent errors by using techniques like lazy loading or prefetching.
Don’t do this:
- Display a loading spinner indefinitely without any indication of progress or potential issues.
- Use technical jargon that the average user won’t understand.
Can you suggest 4 error prevention techniques that align with these principles? Please provide examples of how these techniques have been successfully implemented on other websites to improve user satisfaction.”
Summary
In both examples, contrastive approach provides the AI with a clearer understanding of your expectations and helps it generate more relevant and targeted suggestions. Using this approach, learners will gain insights into the contrasting strategies for handling loading errors, helping them understand how each approach impacts user experience differently. This clear comparison enhances decision-making on which method better suits specific design goals. I hope this gives you an idea on how this style can help you excel in your AI interactions and charge your learning experiences! Have fun!
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.
