Introduction to Chain of Thought (CoT)

Chain-of-thought (CoT) is a prompting technique used to elicit better performance from large language models (LLMs) on complex logical, mathematical, and reasoning tasks.

What is Chain-of-Thought Prompting?

At its heart, CoT asks the language model to break down its reasoning into a series of logical steps. This mimics the way a human would solve a problem by showing their work.

Traditional Prompting vs. CoT

• Traditional: A direct question leading to a direct answer.
• CoT: A question accompanied by a request to “think step by step.”

Why Does CoT Work?

1. Step-wise Verification: The model can double-check its work as it moves through each part of the problem.
2. Intermediate Representations: The model can generate intermediate data that helps it arrive at a more accurate conclusion.
3. Problem Decomposition: CoT helps the model break down complex problems into smaller, more manageable sub-tasks.

Common CoT Variations

1. Few-shot CoT: Providing several examples of questions and their step-by-step reasoning chains in the prompt.
2. Zero-shot CoT: Using a simple instruction like “Let’s think step by step” to trigger the reasoning process.
3. Least-to-Most Prompting: Breaking down a problem into its fundamental sub-problems and solving them sequentially.

Practical Use Cases

• Mathematical Reasoning: Solving complex multi-step word problems.
• Logical Puzzles: Answering riddles or deduction-based questions.
• Code Generation: Writing complex code that requires several layers of logic.