Chain-of-Thought Prompting

Chain-of-Thought Prompting encourages the language model to explicitly show its reasoning process step-by-step before arriving at the final answer.

This technique is particularly powerful for complex problems that benefit from breaking down the reasoning into smaller, more manageable steps. By guiding the model to “think aloud,” you can often get more accurate results and gain insight into how the model is approaching the problem.

Pros:

• Allows the model to break down complex problems into smaller, more manageable steps
• Leads to more logical and accurate reasoning, especially in tasks requiring multi-step inference
• Makes the model’s thinking process transparent, which helps in verifying the correctness of the answer
• Can help identify where reasoning errors occur

Cons:

• Can be more verbose and take up more tokens
• May sometimes include irrelevant or incorrect reasoning steps if not guided effectively
• Requires more careful prompt design to elicit useful reasoning chains

Example:

Prompt:

Solve this math problem and show your work:
A train travels 120 miles at 60 mph. How long does the journey take?

Expected Output:

The formula for time is distance / speed.
The distance is 120 miles and the speed is 60 mph.
So, the time taken is 120 / 60 = 2 hours.

Chain-of-thought prompting works best for:

• Mathematical problems
• Logical reasoning tasks
• Complex decision-making scenarios
• When you want to understand the model’s reasoning process
• When accuracy is critical and you want to verify the logic

When you’re looking to improve your prompting strategy itself, meta prompting can be a valuable technique to explore next.