What is Chain of Thought (CoT)?

Chain of Thought (CoT) is a prompting technique that significantly improves the reasoning capabilities of large language models by instructing them to break down complex problems into explicit intermediate steps before arriving at a final answer. Rather than asking an LLM to produce an answer directly, CoT prompting encourages the model to 'think out loud,' showing its reasoning process step by step. This approach has been shown to dramatically improve performance on mathematical reasoning, logical deduction, multi-step analysis, and other tasks that require sequential thinking.

The technique was popularized by Wei et al. in their 2022 paper 'Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.' They demonstrated that simply adding the phrase 'Let's think step by step' to a prompt (zero-shot CoT) or providing a few examples of step-by-step reasoning (few-shot CoT) could improve accuracy on math word problems from around 18% to 79% with the same model. This finding revealed that LLMs already possess latent reasoning capabilities that can be unlocked through appropriate prompting.

Several variants of chain-of-thought prompting have been developed. Self-Consistency CoT generates multiple reasoning chains and selects the most common answer, improving reliability. Tree of Thought (ToT) explores multiple reasoning branches simultaneously, allowing the model to evaluate and backtrack on different approaches. ReAct combines reasoning traces with action steps (tool use), enabling models to interleave thinking with information gathering. Step-Back Prompting first asks the model to identify the relevant high-level principles before solving the specific problem.

In production AI systems, chain-of-thought reasoning is used extensively. AI coding assistants use CoT to plan implementations before writing code. Customer support AI uses step-by-step reasoning to diagnose issues. Research assistants use CoT to analyze complex questions from multiple angles. Modern reasoning models like OpenAI's o1 and o3, and Anthropic's Claude with extended thinking, have CoT built into their inference process, automatically generating internal reasoning chains that improve output quality even without explicit prompting.