Introduction

Chain-of-Thought (CoT) prompting instructs a language model to spell out intermediate reasoning instead of jumping straight to a conclusion.

• Definition: A prompting style that requests the model to list its thinking process before sharing the final answer.
• Why: Reveals hidden steps, making it easier to validate calculations and catch logic gaps.
• Where: Popular in coding copilots, compliance workflows, analytics bots, and any task requiring human review.

Syntax

[
  {
    "role": "system",
    "content": "You are a careful analyst. Explain your reasoning as numbered steps before the final answer."
  },
  {
    "role": "user",
    "content": "Question: {{problem statement}}\nRemember: Show each step and end with 'Final Answer:'."
  }
]

Example

[
  {"role": "system", "content": "You are a finance tutor. Think step-by-step and show working."},
  {"role": "user", "content": "A store sold 120 gadgets in Q1 and 156 in Q2. What is the percentage growth?"}
]

1. Identify the difference: 156 - 120 = 36
2. Divide by the original quantity: 36 / 120 = 0.3
3. Convert to percentage: 0.3 × 100 = 30%
Final Answer: 30% growth from Q1 to Q2.

[
  {"role": "system", "content": "You are a Python mentor. Reason about the bug before proposing fixes."},
  {"role": "user", "content": "Why does this list comprehension fail? numbers = [int(x) for x in ['4', '7', 'A']]"}
]

Explanation

• System prompt makes the model adopt a teacher persona that values explicit reasoning.
• User message nudges the model to structure thoughts into steps and to label the final answer clearly.
• Intermediate steps give reviewers checkpoints to catch arithmetic slips or unsupported claims.
• Difference from direct prompting: plain prompts yield a single sentence; CoT surfaces the path taken.

Real-World Use Case

A compliance automation team builds an internal assistant that reviews loan applications. With CoT prompting, every approval or rejection includes numbered reasoning tied to policy clauses, allowing risk officers to audit decisions before they are sent to customers.

Key Notes / Tips

• Set expectations like “list the steps” or “explain assumptions” to keep the chain structured.
• Use compact language; overly long reasoning can cost tokens and slow responses.
• Review the chain—models sometimes fabricate intermediate facts even if the final answer looks plausible.
• For production, combine CoT with evaluation scripts that flag missing “Final Answer” markers.
• Consider privacy: redact sensitive data from intermediate reasoning before logging.

Practice Exercise

1. Create a CoT prompt that solves a percentage discount question for retail sales.
2. Draft a CoT prompt that explains the logic for approving or rejecting a software bug fix.
3. Challenge: Combine CoT with a request to cite the policy or document paragraph that supports each step.

Summary

Chain-of-Thought prompting reveals how models think, making decisions traceable and reviewable. Use it whenever the path to the answer matters as much as the answer itself, especially in regulated or high-stakes domains.