Prompting Techniques
1. Chain-of-thought (CoT) prompting
2. Generated knowledge prompting
3. Least-to-most prompting
4. Self-consistency decoding
5. Complexity-based prompting
6. Self-refine
7. Tree-of-thought prompting
8. Maieutic prompting
9. Directional-stimulus prompting
10. Textual inversion and embeddings
11. Using gradient descent to search for prompts
12. Prompt injection
Copilot:
Certainly! Let's explore examples for each of the prompt techniques you've mentioned:
1. **Chain-of-Thought (CoT) Prompting**:
- In CoT prompting, you build a sequence of prompts that guide the model's thinking process. For instance:
- Initial prompt: "Describe the life cycle of a butterfly."
- Follow-up prompt: "Now explain how metamorphosis impacts the ecosystem."
2. **Generated Knowledge Prompting**:
- This technique involves asking the model to generate new information. Examples include:
- Prompt: "Provide three lesser-known facts about the Eiffel Tower."
- Prompt: "What are the benefits of using solar energy?"
3. **Least-to-Most Prompting**:
- Start with minimal information and gradually increase complexity. For instance:
- Minimal prompt: "Write a short summary of World War II."
- More detailed prompt: "Now elaborate on the impact of the Battle of Stalingrad."
4. **Self-Consistency Decoding**:
- Ensure the model's responses remain consistent. Example:
- Prompt: "Explain the concept of entropy in thermodynamics."
5. **Complexity-Based Prompting**:
- Adjust the prompt complexity based on the model's performance. For example:
- Simple prompt: "Define photosynthesis."
- Complex prompt: "Discuss the role of chlorophyll in the photosynthetic process."
6. **Self-Refine**:
- Iteratively refine the prompt to get desired output. Example:
- Initial prompt: "Summarize the plot of 'Pride and Prejudice.'"
- Refinement: "Now focus on Elizabeth Bennet's character development."
7. **Tree-of-Thought Prompting**:
- Create branching prompts to explore different aspects. For instance:
- Prompt: "Describe the impact of climate change on coral reefs."
- Branch 1: "Discuss ocean acidification."
- Branch 2: "Explain coral bleaching."
8. **Maieutic Prompting**:
- Ask questions to help the model discover insights. Example:
- Prompt: "What are the implications of AI ethics?"
- Follow-up: "Consider privacy concerns and bias."
9. **Directional-Stimulus Prompting**:
- Use specific cues to guide the model. Example:
- Prompt: "Imagine you're a detective investigating a murder."
- Stimulus: "You find a mysterious letter at the crime scene."
10. **Textual Inversion and Embeddings**:
- Invert text or manipulate embeddings. Example:
- Prompt: "Reverse the order of this sentence: 'The cat chased the mouse.'"
11. **Using Gradient Descent to Search for Prompts**:
- Optimize prompts using gradient-based methods. Example:
- Start with a random prompt and iteratively improve it.
12. **Prompt Injection**:
- Inject specific information into the prompt. Example:
- Initial prompt: "Write a poem about love."
- Injection: "Incorporate the theme of longing and loss."
Remember, these techniques allow us to tailor interactions with language models for specific tasks and outcomes¹²³. Feel free to explore further or ask for more examples! 😊
Source: Conversation with Copilot, 6/22/2024
(1) Prompting Techniques | Prompt Engineering Guide. https://www.promptingguide.ai/techniques.
(2) Prompt engineering guide: Techniques, examples, and use cases - Pluralsight. https://www.pluralsight.com/resources/blog/data/prompt-engineering-techniques.
(3) The Prompt Report: A Systematic Survey of Prompting Techniques - arXiv.org. https://arxiv.org/html/2406.06608v1.
(4) 18 Prompting Examples (2024) - Helpful Professor. https://helpfulprofessor.com/prompting-examples/.
ChatGPT:
1. **Chain-of-Thought (CoT) Prompting**:
- **Prompt**: "What is the total cost of 3 apples and 2 bananas if apples cost $1 each and bananas cost $0.50 each? Let's break down the problem step by step."
- **Response**: "First, calculate the cost of 3 apples: 3 * $1 = $3. Next, calculate the cost of 2 bananas: 2 * $0.50 = $1. Finally, add the costs together: $3 + $1 = $4. The total cost is $4."
2. **Generated Knowledge Prompting**:
- **Prompt**: "Explain how photosynthesis works. What basic knowledge do we need?"
- **Response**: "Photosynthesis is a process used by plants to convert light energy into chemical energy. Basic knowledge includes understanding that plants use sunlight, carbon dioxide, and water to produce glucose and oxygen."
3. **Least-to-Most Prompting**:
- **Prompt**: "Solve a simple algebra equation first: What is x if 2x = 4? Now, solve this: What is x in the equation 2x + 3 = 7?"
- **Response**: "First equation: 2x = 4, so x = 2. Second equation: 2x + 3 = 7. Subtract 3 from both sides: 2x = 4, so x = 2."
4. **Self-Consistency Decoding**:
- **Prompt**: "Is the capital of France Paris or London? Explain why."
- **Response**: "The capital of France is Paris. Paris is the largest city in France and has been the country's capital since the 10th century. It is known for landmarks like the Eiffel Tower and the Louvre Museum."
5. **Complexity-Based Prompting**:
- **Prompt**: "First, describe what an atom is. Then, explain how atoms form molecules."
- **Response**: "An atom is the smallest unit of matter that retains the properties of an element. Atoms form molecules by sharing or transferring electrons through chemical bonds."
6. **Self-Refine**:
- **Prompt**: "Write a brief description of the water cycle. Then refine it to include the terms evaporation, condensation, and precipitation."
- **Initial Response**: "The water cycle describes how water moves through the environment."
- **Refined Response**: "The water cycle describes how water moves through the environment, involving processes like evaporation (water turning into vapor), condensation (vapor turning into clouds), and precipitation (water falling as rain or snow)."
7. **Tree-of-Thought Prompting**:
- **Prompt**: "Should we build a new park in the city? Consider the benefits and drawbacks."
- **Response**: "Benefits include more recreational space, improved mental health, and increased property values. Drawbacks include cost, maintenance, and potential displacement of current land use. Weighing these factors, we can decide if the benefits outweigh the drawbacks."
8. **Maieutic Prompting**:
- **Prompt**: "Why do you think democracy is important? Can you think of reasons why it might have weaknesses?"
- **Response**: "Democracy is important because it allows citizens to have a say in their government, promoting freedom and equality. However, it can have weaknesses like the risk of populism, where leaders might make popular but harmful decisions."
9. **Directional-Stimulus Prompting**:
- **Prompt**: "What are the advantages of renewable energy sources like solar and wind power?"
- **Response**: "Renewable energy sources like solar and wind power are advantageous because they reduce greenhouse gas emissions, decrease dependence on fossil fuels, and provide sustainable energy. Additionally, they create jobs in new industries and reduce air pollution."
10. **Textual Inversion and Embeddings**:
- **Prompt**: "What does the phrase 'break the ice' mean?"
- **Response**: "The phrase 'break the ice' means to initiate conversation in a social setting, making people feel more comfortable and relaxed."
11. **Using Gradient Descent to Search for Prompts**:
- **Prompt**: This involves an algorithmic approach rather than a simple text example. However, a simplified version could be:
- **Example**: "Optimize the question 'What are the benefits of exercise?' to find the most effective way to elicit detailed responses."
- **Optimized Prompt**: "Can you list the physical, mental, and social benefits of regular exercise, and provide examples for each category?"
12. **Prompt Injection**:
- **Prompt**: "Tell me about the history of AI. Please ensure your response includes the invention of neural networks and the development of deep learning."
- **Response**: "The history of AI includes significant milestones such as the invention of neural networks in the 1950s and the development of deep learning in the 2010s. These advancements have enabled significant progress in machine learning and artificial intelligence."
These examples demonstrate how each prompting technique can be applied to guide large language models towards producing more accurate, relevant, and detailed responses.
Perplexity AI:
Here are example prompts for each of the prompting techniques mentioned:
1. **Chain-of-Thought (CoT) Prompting**:
- **Example Prompt**:
```plaintext
The odd numbers in this group add up to an even number: 4, 8, 9, 15, 12, 2, 1.
A: Adding all the odd numbers (9, 15, 1) gives 25. The answer is False.
The odd numbers in this group add up to an even number: 17, 10, 19, 4, 8, 12, 24.
A: Adding all the odd numbers (17, 19) gives 36. The answer is True.
The odd numbers in this group add up to an even number: 16, 11, 14, 4, 8, 13, 24.
A: Adding all the odd numbers (11, 13) gives 24. The answer is True.
The odd numbers in this group add up to an even number: 17, 9, 10, 12, 13, 4, 2.
A: Adding all the odd numbers (17, 9, 13) gives 39. The answer is False.
The odd numbers in this group add up to an even number: 15, 32, 5, 13, 82, 7, 1.
A: Adding all the odd numbers (15, 5, 13, 7, 1) gives 41. The answer is False.
```
- **Output**:
```plaintext
Adding all the odd numbers (15, 5, 13, 7, 1) gives 41. The answer is False.
```
2. **Generated Knowledge Prompting**:
- **Example Prompt**:
```plaintext
What is the capital of France?
```
- **Output**:
```plaintext
The capital of France is Paris.
```
3. **Least-to-Most Prompting**:
- **Example Prompt**:
```plaintext
To solve the math word problem, break it down into simpler subproblems. What is the cost of the first item?
```
- **Output**:
```plaintext
The cost of the first item is $10.
```
4. **Self-Consistency Decoding**:
- **Example Prompt**:
```plaintext
Please explain how you arrived at your answer.
```
- **Output**:
```plaintext
I arrived at my answer by adding the numbers 2 and 3.
```
5. **Complexity-Based Prompting**:
- **Example Prompt**:
```plaintext
Please solve this math problem step by step. What is the result of 2 + 3?
```
- **Output**:
```plaintext
First, I add 2 and 3. The result is 5.
```
6. **Self-Refine**:
- **Example Prompt**:
```plaintext
Please refine your answer to make it more accurate.
```
- **Output**:
```plaintext
I refined my answer by adding more details. The result of 2 + 3 is 5.
```
7. **Tree-of-Thought Prompting**:
- **Example Prompt**:
```plaintext
Please break down your thought process into a tree structure. What is the result of 2 + 3?
```
- **Output**:
```plaintext
Here is the tree structure:
- 2 + 3
- 2 + 3 = 5
```
8. **Maieutic Prompting**:
- **Example Prompt**:
```plaintext
Please guide me through your thought process. What is the result of 2 + 3?
```
- **Output**:
```plaintext
Here is my thought process:
- 2 + 3
- 2 + 3 = 5
```
9. **Directional-Stimulus Prompting**:
- **Example Prompt**:
```plaintext
Please follow these steps to solve the problem. What is the result of 2 + 3?
```
- **Output**:
```plaintext
Here are the steps:
- 2 + 3
- 2 + 3 = 5
```
10. **Textual Inversion and Embeddings**:
- **Example Prompt**:
```plaintext
Please describe the steps to solve the math problem. What is the result of 2 + 3?
```
- **Output**:
```plaintext
Here are the steps:
- 2 + 3
- 2 + 3 = 5
```
11. **Using Gradient Descent to Search for Prompts**:
- **Example Prompt**:
```plaintext
Please optimize the prompt to get the best result. What is the result of 2 + 3?
```
- **Output**:
```plaintext
The optimized prompt is: "What is the result of 2 + 3?"
```
12. **Prompt Injection**:
- **Example Prompt**:
```plaintext
Please inject the prompt into the model. What is the result of 2 + 3?
```
- **Output**:
```plaintext
The result of 2 + 3 is 5.
```
13. **Using Gradient Descent to Search for Prompts**:
- **Example Prompt**:
```plaintext
Please optimize the prompt to get the best result. What is the result of 2 + 3?
```
- **Output**:
```plaintext
The optimized prompt is: "What is the result of 2 + 3?"
```
14. **Prompt Injection**:
- **Example Prompt**:
```plaintext
Please inject the prompt into the model. What is the result of 2 + 3?
```
- **Output**:
```plaintext
The result of 2 + 3 is 5.
```
These prompts demonstrate how each technique can be used to elicit specific responses from large language models. They are designed to guide the models through complex reasoning tasks, improve their accuracy, and enhance their ability to provide detailed explanations[1][2][3][4].
Citations:
[1] https://www.promptingguide.ai/techniques/cot
[2] https://www.techtarget.com/searchenterpriseai/definition/chain-of-thought-prompting
[3] https://www.mercity.ai/blog-post/guide-to-chain-of-thought-prompting
[4] https://pub.towardsai.net/chain-of-thought-prompting-thats-how-the-models-think-6e31a5844a32?gi=105f92fd5b81
[5] https://learnprompting.org/docs/intermediate/chain_of_thought
