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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.”
You are tasked with solving a complex business problem. Please follow these steps:
1. State the problem clearly.
2. Identify key variables or factors.
3. Explain each factor's potential impact.
4. Develop at least two possible solutions with pros and cons.
5. Choose and justify the best solution.
6. Outline implementation steps.
7. Identify challenges and suggest mitigation strategies.
Remember to show your reasoning and use logical connectors.