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Chain of Thought (CoT) reasoning guides AI models to break down complex tasks into logical, step-by-step processes, improving accuracy, reliability, and explainability of AI responses.

CoT reasoning mimics human problem-solving by encouraging the AI to:

  1. Analyze the problem
  2. Break it down into smaller, manageable parts
  3. Solve each part sequentially
  4. Combine the results to reach a final conclusion

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Implementing CoT in Prompts

1

Explicit Instructions

Tell the AI to think through the problem step-by-step. Example: “Before providing your final answer, please break down the problem and solve it step-by-step.”

2

Question Decomposition

Guide the AI to break down complex queries into smaller, more manageable questions. Example: “To solve this, let’s approach it in stages. First, what are the key components of the problem? Second, how do these components relate to each other? Third, …”

3

Intermediate Steps

Encourage the AI to show its work by providing intermediate results. Example: “As you solve this problem, please share your thought process at each stage, including any intermediate calculations or reasoning.”

4

Logical Connectors

Use words like “therefore,” “because,” “as a result,” to encourage logical connections between steps.

5

Verification Prompts

Ask the AI to verify its own work. Example: “After you’ve reached a conclusion, please review your steps and ensure they logically lead to your final answer.”

Step 2: Importance of context and setting boundariesStep 4: Using few-shot and reinforcing instructions