Chain-of-Thought Prompting

term_id: chain_of_thought_prompting

Category: application_paradigms

Definition

Chain-of-Thought (CoT) prompting improves the performance of large language models on complex reasoning tasks by explicitly asking the model to articulate its step-by-step logic. Instead of jumping directly to a conclusion, the model generates intermediate sentences that represent its thought process. This approach mimics human problem-solving strategies, significantly enhancing accuracy in mathematics, logic puzzles, and multi-step deductions. It can be implemented via few-shot examples or simple instructions like ‘Let’s think step by step.’

Summary

Chain-of-Thought Prompting is a technique that encourages LLMs to generate intermediate reasoning steps before producing a final answer.

Key Concepts

  • Step-by-Step Reasoning
  • Few-Shot Learning
  • Logical Deduction
  • Intermediate Steps

Use Cases

  • Solving mathematical word problems
  • Complex logical reasoning tasks
  • Debugging code generation errors

Code Example

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prompt = "Q: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. If each can has 3 balls, how many does he have?\nA: Roger started with 5 balls. 2 cans of 3 balls each is 6 balls. 5 + 6 = 11. The answer is 11."
print(prompt)

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