In this example, I’ll break down the complex prompt provided into a more Complexity-Based Prompting method. This method demonstrates how to break down complex prompts and gradually build upon them, increasing the level of challenge and detail. For you AI learners, this progression helps illustrate the value of Complexity-Based Prompting as a tool to guide AI into deeper reasoning.
Key Characteristics of Complexity-Based Prompting
- Layered Detailing
Prompts are structured in layers, starting from simple requirements and gradually introducing more specific or complex elements to build on previous information. - Incremental Complexity
Each subsequent layer adds a new aspect, such as context, specific constraints, or real-world examples, creating a nuanced prompt that the AI must parse through at deeper levels. - Progressive Focus
Initial prompts may be broad, but as complexity is added, prompts become highly targeted, guiding the AI to hone in on specific issues or domains. - Contextual Dependency
Each layer builds on the previous context, requiring the AI to remember earlier instructions and adapt responses to incorporate new information cohesively. - Expectation of Real-World Application
At higher levels, prompts request examples or established practices, pushing AI to synthesize responses that align with practical applications, best practices, or industry standards. - Combination of Explanation and Action
Prompts not only request solutions but also explanations of why certain techniques are beneficial, fostering comprehensive responses from the AI. - Analytical Depth
The AI is encouraged to provide deeper insights and rationale, requiring it to make connections between general solutions and specific contexts.
Key Benefits of Complexity-Based Prompting
- Enhanced Response Quality
By layering complexity, responses become richer and more tailored, often resulting in high-quality outputs that address multiple facets of a problem. - Structured Problem-Solving
Complexity-Based Prompting fosters a logical problem-solving structure, helping AI work through issues step-by-step, which can be beneficial for complex inquiries. - Better Alignment with Real-World Scenarios
Through requests for examples and specific applications, responses are more likely to align with practical and real-world applications, making them valuable for training and demonstration purposes. - Depth of Understanding
This approach often yields responses that show a higher level of understanding, as the AI must integrate multiple layers of context, constraints, and examples to form coherent answers. - Encourages AI Adaptability
As new elements are introduced, the AI learns to adapt its responses dynamically, enhancing its flexibility in handling multifaceted queries. - Ideal for Advanced Learning Scenarios
For AI practitioners or trainers, Complexity-Based Prompting offers a way to challenge the AI’s capabilities and assess its understanding, which is useful in educational or professional development settings. - Fosters Critical Thinking in AI Interaction
By prompting the AI to consider why certain approaches work, it can simulate critical thinking, which is beneficial in fields that require nuanced decision-making or justification.
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
This workflow demonstrates how to break down a complex prompt into a series of simpler prompts, gradually increasing complexity and leveraging the AI’s capabilities at each step.
1. Basic Request (Low Complexity)
Prompt:
“I’m designing a website and want to ensure a seamless user experience. Can you suggest 4 error prevention techniques to integrate into my design?”
- Purpose: Starts with a simple ask for error prevention techniques without any specific context.
- Expected Output: General techniques like form validation, clear error messages, loading indicators, and user-friendly UI.
2. Focused Context (Medium Complexity)
Prompt:
“I’m designing a website and want to prevent errors related to website loading latency. Can you suggest 4 error prevention techniques I should integrate to address this specific issue?”
- Purpose: Adds a clear context about website loading latency, narrowing down the scope to a particular issue.
- Expected Output: Techniques such as lazy loading, compressing media files, caching, and providing real-time feedback on page load progress.
3. Expanded with Specificity (Higher Complexity)
Prompt:
“I’m designing a website and want to prevent errors related to loading latency while ensuring users understand why the error is happening. Can you suggest 4 techniques to integrate into the design to prevent latency-related errors and improve user understanding?”
- Purpose: Builds on the previous layer by introducing a new requirement—user understanding of the errors. This increases complexity by requiring the AI to think about error communication and prevention.
- Expected Output: Techniques like detailed error messages explaining latency issues, using progress bars, suggesting user actions (e.g., retry button), and fallback solutions when loading fails.
4. Application in Real-World Scenarios (Advanced Complexity)
Prompt:
“I’m designing a website to prevent errors related to website loading latency and ensure users have a clear understanding of why the error occurs. Can you suggest 4 techniques, with examples of how they’ve been successfully implemented in other designs to improve user satisfaction?”
- Purpose: Now, we demand not only the techniques but also real-world examples. This step requires a more sophisticated understanding of best practices and application.
- Expected Output: The AI might cite examples like Amazon’s use of loading spinners and error messages during high traffic, Google’s caching mechanisms, or YouTube’s use of adaptive streaming for different bandwidths.
5. Expert-Level Complexity (Full Context and Specific Applications)
Prompt:
“I’m designing a website and want to prevent errors related to website loading latency while ensuring users understand what’s happening. Can you suggest 4 specific error prevention techniques, explain how they prevent errors, and provide examples of their successful application in other designs to improve user satisfaction?”
- Purpose: This stage combines all elements: prevention, explanation, and real-world application. It forces the AI to articulate each technique in depth, explaining not only what but how and why it works in specific contexts.
- Expected Output: Detailed examples like Google’s use of “Service Workers” for offline functionality, progressive image loading as used by Medium, or Netflix’s use of adaptive bitrate streaming to ensure smooth playback across different networks.
Complexity-Based Prompting Style Summary
- Low Complexity: General request for techniques.
- Medium Complexity: Introduces a specific problem (loading latency).
- Higher Complexity: Adds user understanding and real-time feedback requirements.
- Advanced Complexity: Requires real-world examples of successful implementations.
- Expert Complexity: Combines all factors and demands a step-by-step, real-world breakdown with explanations.
This workflow provides a practical example of how to apply complexity-based prompting in AI training. By gradually increasing the complexity of the prompts, learners can effectively guide the AI to generate comprehensive and insightful responses during the training of the AI.
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.
