Chain of Thought (CoT) refers to a reasoning pattern in which an AI model generates a sequence of intermediate steps before producing a final answer, rather than jumping directly from prompt to conclusion. The technique emerged from the observation that large language models produce significantly more accurate outputs on complex tasks when they articulate their reasoning explicitly, mimicking the deliberative problem-solving that humans use for arithmetic, logic puzzles, and multi-step planning.

Think of it like showing your work on a math exam: writing down each step not only helps the teacher follow your logic but also forces you to catch errors you would miss if you leapt straight to the answer.

A classic example is asking a model to solve a word problem. Without CoT the model might answer incorrectly. With CoT the prompt either includes example reasoning traces ("few-shot CoT") or simply the phrase "Let's think step by step" ("zero-shot CoT"), producing: "There are 5 apples. 3 are eaten. 5 − 3 = 2. The answer is 2." Benchmarks like GSM8K (grade-school math) show accuracy improvements of 20–40 percentage points on large LLMs when CoT is applied.

CoT is closely related to Prompt Engineering, since the technique is applied at inference time without changing model weights. It complements Zero-Shot reasoning and is often combined with RAG to let models reason over retrieved evidence. Self-consistency—sampling multiple CoT paths and taking a majority vote—further improves reliability. Understanding CoT is foundational for working with Mixture of Experts models and multi-step agent pipelines where reasoning transparency matters.

You will encounter Chain of Thought in research papers on LLM reasoning benchmarks, in system prompts for AI assistants handling customer-support logic, and in agent frameworks that require models to plan before acting.