The full answer
Zero-shot chain-of-thought is asking a model to reason step by step without giving it any worked examples, typically by adding a phrase such as "Let's think step by step." It combines two ideas: zero-shot prompting (instructions only, no examples) and chain-of-thought prompting (intermediate reasoning before the answer).
Origin Kojima and colleagues found that this single phrase substantially improved large models' accuracy on arithmetic and logic benchmarks compared with plain zero-shot prompts, without the effort of writing few-shot reasoning examples. They used two stages: first prompt for the reasoning, then prompt again to extract the final answer from it.
Example
Q: A train leaves at 14:40 and the journey takes 2 hours 35 minutes.
The train is 20 minutes late. When does it arrive?
A: Let's think step by step.
The model reasons: 14:40 + 2:35 = 17:15; plus 20 minutes = 17:35. Then a follow-up such as "Therefore, the arrival time is" extracts the answer, or one prompt asks for the answer on the last line.
How it relates to other terms
| Term | Examples in prompt? | Reasoning written out? |
|---|---|---|
| Zero-shot prompting | No | No |
| Few-shot prompting | Yes | No |
| Few-shot chain-of-thought | Yes, with reasoning | Yes |
| Zero-shot chain-of-thought | No | Yes |
| Self-consistency | Either | Yes, several samples, majority answer |
- Instruction following: it relies on the model understanding and acting on a plain instruction, the core of zero-shot prompting.
- Prompt chaining: the two-stage version is a small chain, reasoning first, then extraction.
- Self-consistency: sampling several zero-shot reasoning paths and voting on the final answer improves accuracy further.
- Output formatting: asking for the answer in a fixed place, or reasoning inside tags, makes it easy to parse.
When to use it
- Multi-step problems with a model that doesn't reason on its own, when you don't have good examples.
- As a quick baseline before writing few-shot reasoning examples.
Limitations and current practice
- Gains are largest for multi-step tasks and large models; simple tasks gain little.
- More output tokens mean more cost and latency.
- Reasoning models now think internally before answering, and their makers say telling them to think step by step is unnecessary and can hurt. For them, state the goal and constraints clearly instead.
- The visible reasoning can look convincing while being wrong, so check the answers.
Last updated 16 October 2026