# Self-Consistency

> Run the same prompt N times at non-zero temperature, aggregate by majority vote — higher accuracy on reasoning tasks with variance as a free confidence signal.

- **Category**: Agentic AI
- **Subcategory**: Verification & Reflection
- **Canonical URL**: https://designpattern.fyi/patterns/self_consistency/

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## Description
**Intent**: Mitigate hallucination on reasoning-heavy tasks by aggregating across multiple independent samples.

**Context**: Your model is mostly right on math word problems and multi-step logic, but occasionally invents a wrong intermediate chain and confidently produces the wrong answer. You can run the same prompt several times in parallel and extract a comparable answer from each.

**Solution**: Run the same prompt N times with non-zero temperature. Extract the answer from each. Aggregate: majority vote for discrete answers, median for numeric, judge for free-form. Sample variance across runs is logged as a confidence signal.


## Use Cases
- Reasoning-heavy questions where the model is mostly right but sometimes invents a wrong chain.
- Answers are extractable in comparable form (discrete, numeric, or judge-able).
- Cost of N samples is acceptable relative to the quality lift.





## Trade-offs


### Advantages

- Higher accuracy on reasoning benchmarks at moderate cost.

- Variance across samples is a free uncertainty estimate — no extra calls needed.




### Considerations & Drawbacks

- Cost scales linearly with N.

- Free-form aggregation requires a judge model — not truly free.







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**Reference**: [Original Source](https://www.agentpatternscatalog.org/patterns/self-consistency/)