Chain of Thought
Make the model think out loud before it answers.
Intent & Description
🎯 Intent
Elicit step-by-step reasoning in the model’s output so it decomposes a problem before answering it.
📋 Context
LLMs trained to predict the next token tend to shortcut to answers. On multi-step math, logic, or planning tasks, this produces confident wrong answers. CoT sidesteps this by forcing the model to externalize its work.
💡 Solution
Add “think step by step” or equivalent to your prompt, or use few-shot examples that demonstrate step-by-step reasoning. The model’s scratchpad becomes part of the output before the final answer token. For API use, some models support a native thinking block (e.g. Claude extended thinking) that keeps the trace separate from the user-facing response. See also: zero-shot-chain-of-thought, extended-thinking, scratchpad.
Real-world Use Case
- Math and logic problems where intermediate steps determine correctness.
- Any task where auditability of reasoning matters (compliance, medical, legal).
- Debugging model failures — the trace shows exactly where reasoning went wrong.
Source
📌 TL;DR
Tell the model to show its work — it gets the right answer more often and you can see why.
Advantages
- Measurably improves accuracy on multi-step tasks with no fine-tuning required.
- Makes model reasoning auditable and debuggable.
- Works zero-shot with modern models — just ask.
Disadvantages
- Increases output token count and therefore cost and latency.
- The reasoning trace can be wrong and still lead to a correct answer (or vice versa).
- Verbose traces can fill context windows on long pipelines.