Prompting Techniques: Problem Splitting and Chunking Techniques

Continuing our series on Prompting Techniques, we delve into two more essential prompting techniques: Problem Splitting and Chunking. These methods are invaluable for breaking down complex issues and managing the AI’s memory constraints, ensuring more detailed and accurate outputs. Let’s explore how to leverage these techniques to become a proficient Prompt Engineer.

Problem Splitting: Tackling Complex Issues

When faced with a complex problem, especially one that feels overwhelming, the best approach is to break it down into smaller, manageable pieces. This technique, known as Problem Splitting, involves dissecting the issue into its components and addressing each part individually. By focusing on one piece at a time, you can build a comprehensive solution step by step.

How to Use Problem Splitting:

1. Identify the Components: Start by listing each component of the problem as its own line in a prompt or explaining each component in its own prompt.
2. Prioritize Key Elements: Determine which components are the most critical and address them first. This is akin to finding the corner pieces of a jigsaw puzzle before completing the edges and filling in the center.
3. Laddering Technique: Begin with broad questions and progressively ask more detailed and specific questions.

Example for Story Creation:

Character Creation:

• Generate some characters.
• What are their backgrounds?
• What are their motivations?
• What conflicts might they have with each other?

Setting:

• Where does the story take place?
• How does the environment impact the story?
• Develop cultural, political, and geographical aspects of the setting.

Plot:

• What’s the overall story arc?
• What are some subplots?
• Break the plot into introduction, rising action, climax, falling action, and resolution.

Practical Exercise:

Use the “AI Problem Splitting Technique” by having the AI ask you questions to flesh out your story:

Prompt: “I have a story idea, but I need your help to discover additional elements of the story. I have a basic premise: ‘Little girl breaks into the home of three bears and causes havoc.’ Please ask me a series of questions to help me flesh out my story. Ask me questions one at a time, do not ask me another question until I have answered. Ask me as many questions as you need to. Once you are done, give me an analysis of the story.”

Chunking: Managing Complex Prompts

One of the biggest challenges with ChatGPT is its memory limitation, measured in tokens. While the memory of ChatGPT has improved vastly since it’s first public release, the system still has its limitation, especially when working with complex or highly detailed prompts or requires the use of extensive outputs.

One common occurrence in these situations, especially as the prompting goes on in a simple instance is that the AI cut off partway through the response due to this limit. The Chunking Technique (not to be confused with the Truffle Shuffle) helps manage this by breaking down the prompt into smaller pieces and compiling the outputs manually.

How to Use Chunking:

1. Break Down the Prompt: Divide the complex prompt into smaller, manageable chunks.
2. Individual Prompts: Submit each chunk as a separate prompt to the AI.
3. Compile Outputs: Manually combine the AI’s responses into a coherent whole.
4. Review and Synthesize: Optionally, ask the AI to analyze the combined outputs or rewrite the information in your own words for consistency.

Example:

Prompt 1: “Explain the basic principles of quantum mechanics.”
Prompt 2: “Describe the key experiments that led to the development of quantum mechanics.”
Prompt 3: “Discuss the practical applications of quantum mechanics in modern technology.”

By breaking the complex topic into smaller parts, you can gather detailed information on each aspect and then compile it into a comprehensive understanding.

Combined Example: Problem Splitting and Chunking

Scenario: You want to create a detailed fantasy world for a novel.

Problem Splitting:

• Characters: “Generate a list of main characters for my fantasy novel.”
• Setting: “Describe the geographical and political landscape of my fantasy world.”
• Plot: “Outline the main plot and subplots of my story.”

Chunking Technique:

Characters:

• “Describe the background and motivations of the protagonist.”
• “Explain the conflicts between the main characters.”

Setting:

• “Detail the cultural aspects of the different regions in my world.”
• “Describe the significant historical events that shaped the current political landscape.”

Plot:

• “Break down the plot into introduction, rising action, climax, falling action, and resolution.”

Combining Outputs: After gathering detailed responses for each chunk, compile the information into a comprehensive document. Review and synthesize the details, ensuring consistency and coherence throughout your narrative.

Differentiating Between Chunking and Laddering Techniques

While both Chunking and Laddering are effective methods for interacting with AI, they serve different purposes and can be used independently or together to achieve optimal results.

Laddering Technique:

Laddering involves starting with simple, broad questions and progressively building up to more complex ones. This method helps establish a foundational understanding before diving into intricate details, allowing the AI to incrementally build on the information provided.

• Process: Start simple → Build complexity → Ask detailed, specific questions
• Purpose: Gradually develop a detailed understanding from a broad base

Example:

1. What is artificial intelligence?
2. What are the different types of artificial intelligence?
3. Can you explain the concept of machine learning?
4. How does deep learning differ from traditional machine learning?
5. What are the future implications of deep learning for society?

Chunking Technique:

To recap, Chunking involves breaking down a complex topic into smaller, manageable parts. Each part is explored separately, and the results are then combined to form a comprehensive understanding of the overall topic. This method is particularly useful for dealing with the AI’s memory limitations and ensuring detailed coverage of each aspect of a complex subject.

• Process: Break complex subject into parts → Explore each part → Combine results
• Purpose: Ensure thorough exploration of complex topics within the AI’s memory constraints

Example:

1. Explain the basic principles of quantum mechanics.
2. Describe the key experiments that led to the development of quantum mechanics.
3. Discuss the practical applications of quantum mechanics in modern technology.

Using Both Techniques Together:

Both techniques can be used together to maximize the depth and accuracy of AI responses. For instance, you might start with Laddering to build up foundational knowledge and then use Chunking to dive deep into specific complex aspects of the topic.

Combined Example:

1. What is the circulatory system? (Laddering start)
2. How does the heart function within the circulatory system?
3. What are the roles of different blood cells?
4. Explain what happens during a heart attack. (Transition to Chunking)
5. Describe preventative measures and lifestyle changes to reduce heart disease risk. (Chunking detailed exploration)

By understanding and leveraging the unique strengths of both techniques, you can effectively guide AI to provide comprehensive and insightful responses to even the most complex queries.

Conclusion

Problem Splitting and Chunking are powerful techniques for managing complex issues and maximizing the effectiveness of AI interactions. By breaking down problems into smaller components and managing the AI’s memory constraints, you can achieve more detailed and accurate outputs. These techniques not only enhance your ability to tackle intricate challenges but also enable a more organized and thorough approach to AI prompting.

Stay tuned for more insights and advanced techniques in our ongoing series on effective AI interaction.