In this example, I’ll break down the complex prompt provided into a more Generated Knowledge Prompting method. In the Generated Knowledge style, we are less focused on explicit, isolated answers. Instead, the AI will be guided through the generation of relevant knowledge before moving to the final outcome, building a chain of reasoning and understanding along the way.
Key Characteristics of Generated Knowledge Prompting Style
- Sequential Knowledge Building: Breaks down a complex task into incremental steps, each aimed at generating and layering knowledge rather than giving an immediate solution.
- Contextual Depth: Encourages the AI to explore underlying contexts and causes before reaching conclusions, fostering more nuanced and informed outputs.
- Iterative Understanding: Each prompt step builds upon the previous, allowing the AI to refine understanding and approach the final answer with comprehensive insight.
- Problem Decomposition: Divides complex prompts into smaller queries, focusing on generating foundational knowledge, specific applications, and user-centered understanding.
- Synthesis of Insights: Concludes by summarizing insights gained throughout the stages, providing a cohesive final output that reflects the layered knowledge generated in earlier steps.
Key Benefits of Generated Knowledge Prompting Style
- Enhanced Output Quality: Produces richer, more accurate answers by allowing the AI to develop a deeper understanding before responding.
- Adaptability to Complex Topics: Effective for multifaceted or nuanced topics where a simple response might lack depth or relevance.
- Greater User Satisfaction: Provides answers that are both informative and contextually relevant, increasing user trust in AI-generated suggestions.
- Increased Robustness in Solutions: Builds a foundation that often reveals alternative approaches or mitigations that might not be apparent in direct prompts.
- Improved Learning and Insight: Learners can gain additional understanding as each stage provides new information, making it valuable for educational purposes or complex problem-solving.
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.”
Original Prompt Breakdown
Request: Examples of successful implementations to increase satisfaction.
Objective: Suggest 4 error prevention techniques.
Specific Focus: Website loading latency and user error comprehension.
Workflow
Step 1: Problem Identification and Knowledge Generation
Prompt:
What are the most common types of errors related to website loading latency that could negatively impact the user experience?
This step generates knowledge about the problem space (errors tied to latency), building foundational information the AI can use later.
Error Prevention Techniques: Methods to stop errors before they occur.
Website Loading Latency: Delays in website content loading.
User Understanding: Clear communication of errors and their causes.
User Satisfaction: Positive user experience despite errors.
Successful Implementations: Real-world examples of these techniques.
Step 2: Generate Knowledge on Prevention Techniques
Prompt:
Based on these common latency-related errors, what general error prevention techniques have proven effective in reducing or mitigating these types of issues?
The AI will pull from general knowledge about error prevention methods that deal specifically with latency, without yet focusing on design.
Other things to consider:
Explicitly request the AI to generate knowledge: “Generate a knowledge base…”
Define the scope of the knowledge: “…focused on error prevention techniques for website design…”
Specify the knowledge organization: “…organized into four distinct categories…”
Direct the AI to elaborate and provide examples: “…with detailed explanations and examples of successful implementations…”
Alternate Regenerated Prompt in GK Style:
“Generate a knowledge base focused on error prevention techniques for website design, organized into four distinct categories. Each category should address website loading latency, ensure user understanding of errors, and contribute to user satisfaction. Provide detailed explanations of each technique and include examples of how they have been successfully implemented in other designs to improve user experience.”
Step 3: Deep Dive into Specific Design Techniques
Prompt:
How can these general error prevention techniques be integrated into web design to enhance user experience and proactively prevent issues with website loading?
This step focuses on taking the general knowledge from step 2 and applying it specifically to web design.
Step 4: User Understanding of Errors
Prompt:
In addition to preventing errors, how can web design ensure that users clearly understand when an error occurs and why it is happening?
Now we explore how to improve user awareness and understanding of errors to enhance satisfaction, using Generated Knowledge around user experience design.
Step 5: Request Examples for Learning
Prompt:
Can you provide examples of successful web designs that implemented these error prevention techniques and improved user satisfaction by helping users understand latency-related issues?
At this point, the AI will pull from its knowledge base to show real-world applications of the previous steps.
Other things to Consider (Facilitate AI Exploration):
Encourage the AI to explore diverse perspectives: “Consider techniques used in various types of websites, including e-commerce, social media, and informational websites.”
Prompt for critical evaluation: “Analyze the strengths and weaknesses of each technique in different contexts.”
Step 6: Synthesize and Deliver Final Suggestions
Prompt:
Based on the understanding of error prevention techniques, design principles, and user comprehension strategies discussed, can you summarize the top 4 techniques that I should implement for my website design?
Now, the AI synthesizes all the generated knowledge into specific recommendations for the user’s context.
Other Things to Consider:
Request specific output formats: “Present the knowledge base in a table format…”
Specify the desired level of detail: “…with each category including a definition, explanation, and at least two real-world examples.”
Step 7: Evolved Generated Knowledge Prompt
The final reworked prompt will provide a much better GK style to the AI so that you can showcase the Key Concepts of the data you are trying to generate:
“Generate a knowledge base focused on error prevention techniques for website design, organized into four distinct categories. Each category should address website loading latency, ensure user understanding of errors, and contribute to user satisfaction. Consider techniques used in various types of websites, including e-commerce, social media, and informational websites. Provide detailed explanations of each technique and include examples of how they have been successfully implemented in other designs to improve user experience. Analyze the strengths and weaknesses of each technique in different contexts.
Present the knowledge base in a table format, with each category including a definition, explanation, and at least two real-world examples.”
Generated Knowledge Prompting Style Summary
This workflow builds a deeper understanding of the problem (errors related to latency), generates practical design approaches for prevention, and ties in user error comprehension before ultimately providing actionable suggestions. The emphasis on knowledge generation throughout the process is a hallmark of the Generated Knowledge Prompting Style. By dissecting the original prompt and systematically transforming it, you can effectively illustrate the key principles of this style. Emphasize the importance of clarity, structure, and explicit instructions in eliciting comprehensive and organized knowledge from the AI.
This hands-on approach will enable you as learners to confidently apply the Generated Knowledge Prompt Style in your own AI training sessions and empower you to generate valuable and insightful knowledge for various applications. 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.
