Today we’re going to delve into the fascinating world of prompt engineering. I’ll be discussing four key concepts that are fundamental to prompt engineering: elements, zero-shot, few-shot, and chain of thought. I’ll also provide examples of each to help illustrate these concepts. Let’s get started!
Elements
In the context of AI, elements refer to the basic building blocks of a prompt.
Instruction – This will be a specific task or instruction you want the model to perform.
This is a conversation between a customer and a happy, helpful ai agent
Context – Can involve external information or additional context to help steer the model to better responses.
Customer question: Can you help me get a refund on my coffee machine Agent answer: Yes, and the best way to do that is by contacting our main office
Input Data – The input or question we are interested in finding a response to.
Question of the customer:
Output Indicator – Indicate the type or format of the output.
Respond to customer with: Yes, and
This will let the model know to response to the custom’s question with a “Yes, and …”
It is recommended to place the instructions at the beginning of the prompt. It may help to use a clear separator such as ### or “”” when separating the instructions and context.
Full example:
This is a conversation between a customer and a happy, helpful ai agent """ Customer question: Can you help me get a refund on my coffee machine Agent answer: Yes, and the best way to do that is by contacting our main office """ Question of the customer: Respond to customer with: Yes, and
Zero-Shot
Zero-shot learning is a type of machine learning where an AI model can solve a task without any prior examples of that specific task. This means the model relies solely on its general understanding and knowledge to generate an appropriate response.
You are directly prompting the model for a response without any examples or demonstrations about the task you want it to achieve. Some large language models do have the ability to perform zero-shot prompting but it depends on the complexity and knowledge of the task at hand.
Example:
Q: What color is the sun? A:
Few-Shot
Few-shot learning, on the other hand, refers to the ability of an AI model to quickly adapt to new tasks with very limited examples. This is especially relevant in situations where obtaining a large amount of labeled data is difficult or expensive.
A sentiment classification example:
Q: The movie was great! A: Positive Q: That ice cream store was horrible. A: Negative Q: Those are cows. A: Neutral Q: Roller coaster are the best! A:
Chain of Thought
Chain of thought is an AI concept that refers to the ability of a model to reason and think through a series of related ideas or steps in order to solve a problem or generate a response. This can be particularly important for tasks that require complex problem-solving, reasoning, or creativity.
Q: 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. Q: The odd numbers in this group add up to an even number: 15, 32, 5, 13, 82, 7, 1. A:
Understanding these four concepts—elements, zero-shot, few-shot, and chain of thought—provides a foundation for appreciating the intricacies of artificial intelligence. As AI continues to develop and play a greater role in our lives, it’s important for us to learn these fundamental ideas and their applications.