Chain-of-Thought Prompting Pattern

🎯 Difficulty Level: Easy
⏱️ Reading Time: 5 minutes
👤 Author: Rob Vettor
📅 Last updated on: February 17, 2025

What is Chain-of-Thought (CoT) Prompting?

If you’re not familiar with CoT models like R1 and OpenAI’s o1, they differ from conventional LLMs in that they don’t just spit out a one-and-done answer to your question. Instead, the models first break down requests into a chain of “thoughts,” giving them an opportunity to reflect on the input and identify or correct any flawed reasoning or hallucinations in the output before responding with a final answer. Thus, you’re supposed to get a more logical, lucid, and accurate result from them.

Chain of Thought fixes this. Instead of spitting out an answer, the model explains its reasoning step by step. If it makes a mistake, you can see exactly where. More importantly, the model itself can see where.

This is more than a debugging tool. It changes how models think. The act of explaining forces them to slow down and check their own work. The result is better answers, even without extra training.