What is Chain Of Thought Prompting? How it Works?

What is Chain Of Thought Prompting? How it Works?

Chain of thought prompting is a technique used in prompting large language models like GPT-4 to generate more coherent and logically consistent responses. This approach involves breaking down the reasoning process into a series of steps, which helps the model better understand and respond to complex queries.

Here’s a basic outline of how it works:

1. Clarify the Question: Ensure the question is fully understood. If the question is ambiguous, specify or ask for clarification.
2. Identify Relevant Information: Highlight or gather all relevant information needed to address the question.
3. Break Down the Problem: Decompose the problem into smaller, manageable parts or steps.
4. Reason Through Each Step: Methodically work through each step, ensuring that the logic is sound and that all aspects of the problem are considered.
5. Synthesize the Answer: Combine the results of each step to form a coherent and comprehensive answer to the original question.

Example

Question: Why does the sun appear to rise in the east and set in the west?

Chain of Thought:

1. Clarify the Question: The question asks about the apparent motion of the sun across the sky.
2. Identify Relevant Information: Earth’s rotation, direction of rotation, observer’s perspective.
3. Break Down the Problem:
   • The Earth rotates on its axis.
   • The direction of Earth’s rotation is from west to east.
   • As the Earth rotates, different parts of the world experience sunlight.
4. Reason Through Each Step:
   • Because the Earth rotates from west to east, the sun appears to move in the opposite direction.
   • To an observer on Earth, this rotation makes the sun appear to rise in the east and set in the west.
5. Synthesize the Answer: The sun appears to rise in the east and set in the west because the Earth rotates on its axis from west to east, causing the sun to appear to move across the sky in the opposite direction.

Using chain of thought prompting can greatly improve the quality and accuracy of the responses generated by a language model. It encourages systematic thinking and helps in managing complex queries effectively.