Chain-of-thought versus few-shot prompting
What they are and when to use each one
Recently, I was listening to an episode of Dan Shipper's "How Do You Use ChatGPT?" Dan was interviewing Nathan Labenz on how he uses ChatGPT as a "copilot" for learning. As always, the episode was interesting. One thing that caught my attention was the discussion of chain-of-thought versus few-shot prompting. Fair warning: This is a little advanced, although I've done my best to keep things straightforward and easy to understand.
Few shot prompting is pretty easy to understand. Just follow your task description with a few examples.
Chain of thought prompts are more complex. Briefly, chain of thought prompting basically asks ChatGPT (or another generative AI chatbot) to think aloud. Here's an example of a chain of thought prompt that asks ChatGPT to explain chain of thought prompting. (By the way, I asked ChatGPT to create this prompt for me, which can be a good way to develop prompts.)
[Prompt]
Explain the concept of chain of thought prompting using the chain of thought approach. Begin by defining what chain of thought prompting is. Next, break down the process into its key components, explaining each one step by step. Then, illustrate how these components work together to guide an AI in processing and responding to complex tasks. Finally, conclude by summarizing the advantages of using chain of thought prompting in AI interactions, especially in educational contexts.
Here's the result:
[Response]
To explain the concept of chain of thought prompting using the chain of thought approach, let's start by defining what chain of thought prompting is. Chain of thought prompting is a method of interacting with AI models where the user guides the AI through a series of logical steps or a thought process. This approach is used to tackle complex problems or questions that require more than a simple, direct answer.
Now, let's break down the process into its key components and explain each one step by step:
Identifying the Problem: The first step is to clearly state the problem or question that needs to be addressed. This sets the stage for the AI to understand what it needs to solve.
Decomposing the Problem: Here, the problem is broken down into smaller, more manageable parts. This step is crucial because it simplifies the problem, making it easier for the AI to process and address each part sequentially.
Sequential Reasoning: The AI then goes through each part of the problem, applying logical reasoning. This involves step-by-step processing, where the AI not only identifies relevant information but also explains how it is using this information to reach a conclusion.
Synthesizing the Solution: After addressing each part, the AI synthesizes the information from all the steps to form a comprehensive answer or solution to the original problem.
Reflection or Further Implications: Optionally, the AI can reflect on the solution, providing insights into its significance or the implications of the solution.
Now, let's illustrate how these components work together to guide an AI in processing and responding to complex tasks. When presented with a complex problem, the AI first understands and defines the scope of the problem. It then breaks the problem down into smaller steps, addressing each step with logical reasoning. This process is transparent, allowing the user to see how the AI is thinking and how each step leads to the next. Finally, the AI combines all these steps to present a final solution that is not only accurate but also explained in a way that is easy to understand.
In conclusion, the advantages of using chain of thought prompting in AI interactions, especially in educational contexts, include promoting a deeper understanding of the problem-solving process, enhancing the transparency of the AI's thought process, and potentially leading to more accurate and detailed answers. This approach is particularly beneficial in education, as it mirrors the way humans solve problems, making it a valuable tool for teaching and learning complex concepts.
Wow! That's a lot of explanation.
So, which should you use? Well, that depends. For most tasks, I think few-shot prompting works pretty well. But few-shot prompts require knowing what good output will look like. That's not always the case; when it is, you might want to try chain-of-thought prompting. Also, you'll get better results with chain-of-thought if you're tackling a complex problem.
If you're a beginner, stick with few-shot prompts. Even one-shot prompts, which include only a single example will work well for many tasks. I often don't include an example at all. If the results aren't appropriate, I'll try a different approach.
There are two messages here. First, keep things simple when you can. Simple is often effective. Second, don't be afraid to experiment with different approaches and even to blend approaches. Prompting can be simple or complex. The choice is yours.