By encouraging the model to generate intermediate reasoning steps before arriving at a final answer, you can achieve more accurate and insightful responses.
Chain-of-Thought (CoT) prompting involves guiding the model to articulate a step-by-step reasoning process when answering a question or solving a problem. Instead of providing a direct answer, the model is encouraged to “think out loud,” detailing the intermediate steps that lead to the final conclusion.
Example 1: Mathematical Problem Solving.
By requesting a step-by-step solution, the model demonstrates the calculation process, reducing the chance of errors.
Prompt:
Solve the following math problem step-by-step:
If one book costs $15 and you buy 4 books, how much do you spend in total?
Answer:
Model’s response:
Each book costs $15. If you buy 4 books, the total cost is $15/book * 4 books = $60. So, you spend $60 in total.
Example 2: Logical Reasoning.
The chain-of-thought reasoning clarifies the relationships, leading to the correct conclusion.
Prompt:
Provide a detailed explanation to solve this logic puzzle:
Tom, Sarah, and Bill are siblings. Tom is older than Sarah. Sarah is older than Bill. Who is the youngest?
Answer:
Model’s response:
Tom is older than Sarah, and Sarah is older than Bill. This means Tom is the oldest, Sarah is in the middle, and Bill is the youngest. Therefore, Bill is the youngest.
Example 3: Decision Making.
The model walks through the decision-making process, providing justification for the recommendation.
Prompt:
Help me decide whether to take an umbrella today. The weather forecast says there’s a 60% chance of rain.
Answer:
Model’s response:
A 60% chance of rain means it’s more likely than not to rain. To stay dry, it’s a good idea to take an umbrella. Therefore, you should take an umbrella today.
Need more help?
Additional questions or feedback? Reach out to help@helicone.ai or schedule a call with us.
By encouraging the model to generate intermediate reasoning steps before arriving at a final answer, you can achieve more accurate and insightful responses.
Chain-of-Thought (CoT) prompting involves guiding the model to articulate a step-by-step reasoning process when answering a question or solving a problem. Instead of providing a direct answer, the model is encouraged to “think out loud,” detailing the intermediate steps that lead to the final conclusion.
Example 1: Mathematical Problem Solving.
By requesting a step-by-step solution, the model demonstrates the calculation process, reducing the chance of errors.
Prompt:
Solve the following math problem step-by-step:
If one book costs $15 and you buy 4 books, how much do you spend in total?
Answer:
Model’s response:
Each book costs $15. If you buy 4 books, the total cost is $15/book * 4 books = $60. So, you spend $60 in total.
Example 2: Logical Reasoning.
The chain-of-thought reasoning clarifies the relationships, leading to the correct conclusion.
Prompt:
Provide a detailed explanation to solve this logic puzzle:
Tom, Sarah, and Bill are siblings. Tom is older than Sarah. Sarah is older than Bill. Who is the youngest?
Answer:
Model’s response:
Tom is older than Sarah, and Sarah is older than Bill. This means Tom is the oldest, Sarah is in the middle, and Bill is the youngest. Therefore, Bill is the youngest.
Example 3: Decision Making.
The model walks through the decision-making process, providing justification for the recommendation.
Prompt:
Help me decide whether to take an umbrella today. The weather forecast says there’s a 60% chance of rain.
Answer:
Model’s response:
A 60% chance of rain means it’s more likely than not to rain. To stay dry, it’s a good idea to take an umbrella. Therefore, you should take an umbrella today.
Need more help?
Additional questions or feedback? Reach out to help@helicone.ai or schedule a call with us.