Chain of Thought

Chain of thought (CoT) is a technique where you encourage an AI model to work through a problem step by step rather than jumping straight to the answer. It turns out that when you ask an AI to "think out loud," it makes fewer mistakes, especially on math, logic, and reasoning problems.

Here is why it works: when a model tries to answer a complex question in one shot, it can easily make logical errors. But when it breaks the problem into smaller steps, each step is simpler and more likely to be correct. It is the same reason your math teacher told you to show your work -- the process of writing out each step helps you catch mistakes.

You can trigger chain of thought in several ways. The simplest is just adding "Let's think step by step" to your prompt. You can also show the model an example of step-by-step reasoning and ask it to follow the same pattern. Some newer models, like OpenAI's o1 series, have chain-of-thought reasoning built in -- they automatically reason through problems internally before giving you an answer.

Chain of thought is especially useful for word problems, multi-step calculations, coding challenges, logical puzzles, and any task where the answer depends on getting multiple intermediate steps right. It does use more tokens (because the model is writing out more text), but the improvement in accuracy is usually worth the extra cost.