In this example, I’ll break down the complex prompt provided into a more ReAct Prompting method. ReAct (Reason + Act) is a prompting style that encourages Large Language Models (LLMs) to approach tasks by first reasoning about the problem and then taking a series of actions to solve it. This is achieved by explicitly prompting the LLM to think through the steps involved, consider different options, and justify its decisions. ReAct is particularly useful for complex tasks that require multi-step problem-solving, critical thinking, and decision-making. It can be applied to a wide range of applications, including:
- Code generation
- Data analysis
- Creative writing
- Task planning
- Question answering
Key Characteristics
- Sequential reasoning with direct action steps.
- Iterative approach, where each step builds on the previous.
- Prioritizes logical decision-making and analysis before presenting a solution.
- Guides the model through reasoning tasks, followed by tangible actions or suggestions.
Key Benefits
- Improves structured problem-solving by focusing on incremental steps.
- Increases accuracy, particularly with multi-part prompts.
- Helps avoid premature conclusions by promoting a step-by-step solution-building approach.
- Useful for users who need clear reasoning behind each recommendation.
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
Step 1: Identify and Analyze the Core Problem Areas
Prompt:
“Let’s identify the key areas of concern: (1) reducing website loading latency errors, and (2) providing users with clear, informative error messages when issues arise. Could you begin by reasoning through common challenges in these areas?”
Reasoning:
The AI considers common issues that users experience due to website loading delays and explores why clarity in error messages is critical for user satisfaction.
Step 2: Suggest Initial Techniques Based on Common Challenges
Prompt:
“Based on the challenges we’ve identified, what are some initial design techniques that could help prevent loading latency errors and provide user-friendly error explanations? Please explain how each technique addresses specific challenges.”
Reasoning + Action:
The AI suggests targeted techniques (e.g., lazy loading, content delivery network (CDN) integration), explaining how each one mitigates loading delays and aligns with user expectations.
Step 3: Request Examples of Successful Implementations
Prompt:
“Could you provide examples of successful implementations for each technique you’ve suggested? Focus on how these examples show improved loading performance and user satisfaction.”
Reasoning + Action:
The AI references case studies or widely recognized websites, describing specific error prevention techniques and their impact on user satisfaction (e.g., Facebook’s lazy loading images or Google’s use of CDNs).
Step 4: Refine Techniques for Clear Error Messaging
Prompt:
“For each technique, could you also suggest ways to improve the clarity of error messages if a loading issue does occur? Consider approaches that would help users understand the issue and feel supported.”
Reasoning + Action:
The AI explores methods like custom error codes, friendly language, and tips for retrying or refreshing, ensuring users feel informed rather than frustrated by errors.
Summary
This ReAct prompting style enables the AI trainer to witness how each reasoning step and action connects, offering an incremental and thoughtful approach to generating practical design solutions. This ReAct prompt guides the LLM to approach the task systematically, ensuring a more comprehensive and well-reasoned response. By explicitly prompting for reasoning and action, we encourage the LLM to generate more accurate, transparent, and insightful suggestions.
So, go out there and get your ReAct(ion) while training your AI and learning some new skills! 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.
