Chain-of-Thought Prompting #

Chain-of-Thought (CoT) prompting is a technique that guides language models to break down complex reasoning into explicit, intermediate steps, resulting in more accurate responses to questions requiring multi-step reasoning.

When to Use It #

• When solving problems requiring logical reasoning
• For mathematical calculations
• When complex decision-making is needed
• For tasks requiring step-by-step analysis

Implementation Approach #

Basic Structure: #

[Question that requires reasoning]
Let's think through this step by step:

Example: #

Without Chain-of-Thought:

Q: If John has 5 apples and gives 2 to Mary, then buys 3 more but uses 4 to make a pie, how many apples does John have left?
A: 2 apples

With Chain-of-Thought:

Q: If John has 5 apples and gives 2 to Mary, then buys 3 more but uses 4 to make a pie, how many apples does John have left?
A: Let's think through this step by step:
1. John starts with 5 apples
2. John gives 2 apples to Mary, leaving him with 5 - 2 = 3 apples
3. John buys 3 more apples, so now he has 3 + 3 = 6 apples
4. John uses 4 apples to make a pie, leaving him with 6 - 4 = 2 apples
Therefore, John has 2 apples left.

Benefits #

• Improved Accuracy: Breaking down complex problems leads to fewer reasoning errors
• Transparency: The reasoning process is visible and can be checked
• Adaptability: Works across different types of reasoning tasks
• Reduced “Hallucinations”: The structured approach minimizes incorrect logical leaps

Advanced Techniques #

Self-Consistency with CoT #

Generate multiple reasoning paths and take the majority answer for even higher accuracy.

Zero-shot CoT #

Simply adding “Let’s think step by step” to prompts often triggers chain-of-thought reasoning even without examples.

Least-to-Most Prompting #

Break complex problems into easier subproblems and solve them sequentially.

Best Practices #

1. Be explicit about requesting step-by-step reasoning
2. For complex problems, consider providing an example of the reasoning process
3. Encourage the model to evaluate its own reasoning as a final step
4. Use clear formatting to separate steps (numbering or bullet points)