Problem Solving with GenAI

Introduction

The Learn Prompting Method is a structured approach for problem-solving with Generative AI. It helps users determine whether Gen AI is the right solution for a problem and guides them through applying prompt engineering, selecting tools, and refining their approach. In this document, we'll walk through the five steps of the method and apply it to a case study to demonstrate its effectiveness.

1. The Five Steps of the Learn Prompting Method
   • 1. State Your Problem
   • 2. Examine Relevant Information
   • 3. Propose a Solution
   • 4. Adjust the Solution
   • 5. Launch Your Solution
2. Case Study: Creating a Hat Information Bot
3. Conclusion
4. FAQ

The Five Steps of the Learn Prompting Method

The Learn Prompting Method consists of five steps: State Your Problem, Examine Relevant Information, Propose a Solution, Adjust the Solution, and Launch Your Solution. Let's explore each step in more detail.

1. State Your Problem

The first step is to clearly define the problem you're trying to solve. Focus on the issue itself without jumping to potential solutions. For example:

Our customers have frequent questions about our product's features, and we need a way to address these queries to prevent losing potential business.

This clear problem statement helps you stay focused and avoid unnecessary complexity.

2. Examine Relevant Information

Next, research and gather data that is relevant to your problem. This can involve looking at similar problems, studying your audience or product, and analyzing available tools and prompts. At this stage, you'll also determine if Gen AI is suitable for addressing the issue.

For example, in the hat information bot case study, this step would involve reviewing common customer questions, analyzing chatbot performance, and exploring relevant AI tools.

3. Propose a Solution

Once you've gathered the necessary information, it’s time to propose a solution. This could be a specific AI prompt, a new tool, or a strategy for using existing tools more effectively. Ensure that the proposed solution is directly linked to the problem and backed by your research.

For instance, you might propose creating a chatbot using a pre-trained language model to answer specific types of customer queries.

4. Adjust the Solution

After selecting a solution, test and refine it based on feedback. This iterative process might involve user testing, analyzing interactions, and fine-tuning your prompts or tools to improve the results. Prompt engineering often plays a key role here, helping you refine how AI responds to user input.

For example, after testing the chatbot, you may adjust the prompt to make it more engaging or tailored to different types of users.

5. Launch Your Solution

Finally, once you've refined the solution, it’s time to launch it. This could mean integrating it into your product, deploying it on a platform, or starting to use it in customer interactions. Be sure to continue monitoring its performance and make further adjustments as needed.

The Learn Prompting Method is cyclical, meaning that after launching, you should keep iterating based on feedback and new insights.

Case Study: Creating a Hat Information Bot

To illustrate the Learn Prompting Method, let's go through a case study of creating a chatbot that answers customer queries about hats.

1. State your problem: We have a large volume of user queries about hats, their history, and how to wear them. We need to provide answers to prevent losing potential business.

2. Examine relevant information: We analyze common queries, which focus on the history, styling, and care of hats. We also look at available chatbot tools, their context limitations, and pricing.

3. Propose a solution: We propose using ChatGPT to create a bot that answers questions in a friendly and informative manner:

4. Adjust the solution: After testing with a small group, we adjust the prompt to make it more engaging and segment responses based on user interest (formal history vs. informal style). We create a routing prompt to determine the user’s intent and direct them to the appropriate bot response.

We adjust our prompts accordingly:

We do even more user testing and realize that we need to segment our market: people interested in hat history prefer the more formal approach, while those interested in style and wearing the hat would prefer a more informal bot. We develop an initial routing prompt that decides which type of user they are based on their question:

5. Launch your solution: We launch the chatbot on the company’s website and continue to monitor and adjust its performance based on user feedback.

Conclusion

The Learn Prompting Method is a practical approach to problem-solving with Generative AI. By following its five steps—State the Problem, Examine Relevant Information, Propose a Solution, Adjust the Solution, and Launch the Solution—you can effectively create and refine AI-driven solutions, such as chatbots, to meet your specific needs.

FAQ

Why should I follow the Learn Prompting Method?

The method provides a structured, efficient approach to problem-solving, ensuring you assess whether Gen AI is appropriate and guiding you through refining and launching a well-optimized solution.

What are the five steps of the Learn Prompting Method?

The five steps are: State Your Problem, Examine Relevant Information, Propose a Solution, Adjust the Solution, and Launch Your Solution.

Can this method be applied to non-chatbot problems?

Yes, the Learn Prompting Method is versatile and can be applied to a wide range of problems, whether they involve chatbots, content generation, or other AI-driven solutions.

Valeriia Kuka

Valeriia Kuka, Head of Content at Learn Prompting, is passionate about making AI and ML accessible. Valeriia previously grew a 60K+ follower AI-focused social media account, earning reposts from Stanford NLP, Amazon Research, Hugging Face, and AI researchers. She has also worked with AI/ML newsletters and global communities with 100K+ members and authored clear and concise explainers and historical articles.