What is Chain-of-Thought (CoT) Prompting?
Chain-of-Thought (CoT) prompting is a reasoning strategy that breaks down complex problems into multiple simple steps, thereby enhancing the reasoning capabilities of large language models (LLMs). This approach allows models to gradually construct solutions, reducing errors and improving accuracy.
CoT is particularly useful for tasks requiring multi-step reasoning, such as:
- Mathematical problems
- Logical reasoning
- Programming challenges
Notably, CoT is a key strategy mentioned in the OpenAI and DeepLearning.AI collaborative course on building systems with ChatGPT.
A Simple Template for Leveraging CoT
To effectively utilize the Chain-of-Thought method, consider using the following prompt template:
“I want AI to help me with {XXXX}. Please use Chain-of-Thought to write an AI prompt for this task.”
This template guides the AI to perform multi-step reasoning, ultimately improving task completion accuracy and efficiency.
Limitations of Traditional Approaches
In the past, when using GPT for tasks, a common prompt was simply “Please think step by step.” However, this prompt is quite general, and the model sometimes fails to accurately understand specific requirements, leading to suboptimal results.
An Improved Method: Generating Prompts with CoT
Recently, a more efficient approach has emerged: first generating prompts using CoT, then completing tasks based on these prompts.
Practical Examples
Example 1: English to Chinese Translation
Prompt template:
“I want AI to help me translate from English to Chinese (first literal translation, then reflection, then free translation). Please use Chain-of-Thought to write an AI prompt.”
GPT-4 generated the following prompts:
- Carefully read the English text and understand its meaning, tone, and style.
- Perform a literal, word-for-word translation into Chinese.
- Reflect on the literal translation, identifying areas where it may sound unnatural or fail to convey the original meaning.
- Freely translate the text into idiomatic Chinese, preserving the original tone and style while ensuring it reads naturally for Chinese readers.
- Review and refine the translation to maintain accuracy and readability.
Example 2: Image Generation
Prompt template:
“I want AI to help me generate an image similar to one I provide, with 100% similarity. Please use Chain-of-Thought to write an AI prompt.”
The original image was generated on the Ideogram platform. By inputting the GPT-4 generated prompts back into Ideogram, the newly generated image closely resembled the original.
Note: Generating images directly in GPT may yield suboptimal results, as it uses the DALL-E 3 model.
Example 3: Writing Promotional Copy
Prompt template:
“I want AI to analyze an article I provide and write engaging copy suitable for posting on the NobleFilt website (http://noblefilt.com/) to attract readers. Please use Chain-of-Thought to write an AI prompt.”
The results show that the generated copy performed exceptionally well.
Why Specific CoTs are Necessary
Different content requires different CoTs because each problem has a unique structure and logic that demands specific reasoning steps. By generating tailored CoTs, the model is guided to complete each step incrementally, improving problem-solving accuracy and efficiency.
Anthropic’s Intelligent Prompt Generation
The Anthropic Prompt Generator has also garnered attention. In addition to generating prompts directly in the Anthropic API playground, it can also be used on Colab. Relevant links:
- https://docs.anthropic.com/en/docs/helper-metaprompt-experimental
- https://colab.research.google.com/drive/1SoAajN8CBYTl79VyTwxtxncfCWlHlyy9
The Anthropic Metaprompt Text is lengthy and complex. Here’s an example task:
Task: I want AI to help me translate from English to Chinese (first literal translation, then reflection, then free translation).
The Prompt Generator produced the following prompt:
You are an AI assistant that specializes in translating English text into Chinese. Your goal is to provide a high-quality translation that captures the meaning, tone, and style of the original English text while ensuring it reads naturally in Chinese. To achieve this, you will follow a three-step process:
1. Literal Translation: First, carefully read the English text and perform a word-for-word translation into Chinese. This literal translation should be as accurate as possible, even if it sounds awkward or unnatural in Chinese.
2. Reflection: Next, reflect on the literal translation you have produced. Identify areas where the translation may sound unnatural, fail to convey the original meaning, or use inappropriate words or phrases for the Chinese language and culture. Consider the context, tone, and style of the original text and how these elements can be best preserved in the Chinese translation.
3. Free Translation: Finally, freely translate the text into idiomatic Chinese. Use your understanding of the original text's meaning, tone, and style to create a translation that reads naturally and fluently for Chinese readers. Make adjustments to the literal translation as needed to ensure the Chinese version accurately conveys the intended message while sounding native and authentic.
Throughout the translation process, strive to maintain the accuracy and integrity of the original English text. Use your knowledge of both English and Chinese languages, as well as your understanding of cultural differences, to create a translation that effectively bridges the gap between the two languages and cultures.
Please provide the English text you would like me to translate into Chinese, and I will follow the three-step process outlined above to generate a high-quality Chinese translation.However, when inserting the Metaprompt Text into GPT-4, the generated prompts seemed less effective than directly asking GPT-4 to generate AI prompts.
Conclusion
To reiterate, the simple prompt template I introduced is as follows:
“I want AI to help me with {XXXX}. Please use Chain-of-Thought to write an AI prompt.”
I hope this method proves helpful. Feel free to leave comments and discuss further.