Chain of Thought
Prompting technique that asks an LLM to reason step-by-step before answering.
Chain of thought (CoT) is the practice of asking an LLM to show its reasoning before producing a final answer. The simple version is a prompt like "think step by step" or "reason through this carefully before answering." The model produces intermediate reasoning, then the answer.
Why it works: forcing the model to spend tokens on intermediate steps is a form of compute allocation. Hard problems benefit from more compute; easy problems don't need it. CoT lets the model take more or less depending on what the problem needs.
In 2026, modern reasoning models (OpenAI's o-series, Claude with extended thinking) bake CoT into their architecture — they reason internally before outputting an answer, often invisibly. For operators, the practical takeaway: pick reasoning models for hard problems (math, code generation, multi-step planning), faster non-reasoning models for simple tasks. Reasoning models are slower and cost more but get harder problems right.