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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