Chain of Thought: Teaching AI to Show Its Work

If you ask a math student for the answer to a complex calculus problem, and they just shout "42!", you can't be sure if they solved it or guessed. If they get it wrong, you don't know why. The same applies to AI.

Large Language Models (LLMs) are probabilistic. They predict the next word. Sometimes, they try to jump straight to the answer without doing the intermediate reasoning. This leads to "hallucinations" or confident errors.

The "Thinking" Process

Chain of Thought (CoT) prompting is a technique where you encourage the AI to break down a problem into intermediate steps before arriving at a final answer. It forces the model to "show its work."

Research from Google and OpenAI has shown that for logic, math, and reasoning tasks, CoT significantly improves accuracy. It allows the model to correct itself as it goes.

How to Use It

The simplest way to trigger CoT is to add a magic phrase to your prompt:

"Let's think step by step."

This simple instruction acts as a trigger. It tells the model to switch from "retrieval mode" (guessing the answer) to "reasoning mode" (deriving the answer).

Example: The Apple Problem

Prompt: "I have 3 apples. I eat 1. I buy 5 more. I give half to my friend. How many do I have?"

Why It Matters

As we integrate AI into critical workflows (legal, medical, coding), accuracy is paramount. Chain of Thought is a simple, zero-cost way to make your AI assistant smarter and more reliable. It turns a black box into a glass box.