Chain of Thought
A prompting technique that encourages an LLM to reason step by step before giving a final answer, dramatically improving performance on complex tasks.
Chain of Thought (CoT) prompting was popularized by the 2022 Google paper 'Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.' The core insight is that asking a model to 'think step by step' causes it to generate intermediate reasoning traces that dramatically improve accuracy on multi-step problems like math, logic, and science questions.
CoT works because the intermediate tokens serve as a scratchpad — each step in the reasoning creates context for the next step, allowing the model to tackle problems that exceed its ability to solve in a single forward pass. The technique essentially lets the model allocate more compute to hard problems by spending more tokens thinking about them.
Modern reasoning models like OpenAI's o-series and Anthropic's Claude 3.7 Sonnet with extended thinking take this further, performing extended internal CoT before generating a response. These models learn through reinforcement learning to produce useful reasoning traces, rather than relying on prompting alone.
Read next
Reasoning Models and Chain of Thought: AI That Thinks
How reasoning models work, why they're so much better at hard problems, the key models in the space, and when to use them over standard LLMs.
RLHF: How AI Models Learn to Be Helpful
Reinforcement Learning from Human Feedback — the training technique behind ChatGPT and Claude that shaped modern AI assistants to be helpful, harmless, and honest.
Prompt Engineering: The Complete Guide
Master the art and science of writing effective prompts — from basic techniques to advanced methods like chain-of-thought, few-shot learning, and structured output generation.