# Self-consistency

**Source:** https://promtable.com/glossary/self-consistency

> Self-consistency runs the same prompt multiple times at non-zero temperature and picks the most common final answer, raising accuracy on reasoning tasks.

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Self-consistency runs the same prompt multiple times at non-zero temperature and picks the most common final answer, raising accuracy on reasoning tasks.

Introduced by Wang et al. (2022), self-consistency exploits the fact that reasoning chains often disagree along the way but converge on the same final answer if the prompt is good. You sample N reasoning chains, extract the final answer from each, and take the majority vote (or a weighted average for numeric answers). It is most useful on math, logic, and multi-step planning tasks where there's a discrete right answer. Cost scales linearly with N — production deployments usually pick N=3–5.

## When to use

- Math, logic, multi-step QA where there's one correct answer.
- Cost-tolerant inference where 3–5× compute is acceptable.

## Common mistakes

- Using it on open-ended generation — no notion of 'majority'.
- Setting temperature to 0 (then all samples are identical).

## Related terms

- [chain-of-thought](https://promtable.com/glossary/chain-of-thought)
- [temperature](https://promtable.com/glossary/temperature)

## Sources

- [Wang et al. 2022 (arXiv)](https://arxiv.org/abs/2203.11171)

*Last updated: 2026-06-01*
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