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Sampling the model several times on the same prompt and taking the majority answer to beat a single noisy guess.
Self-consistency runs the same chain-of-thought prompt multiple times at a non-zero temperature, then picks the answer that shows up most often across the samples. Different reasoning paths tend to converge on the correct answer while errors scatter, so voting filters out one-off mistakes. It reliably lifts accuracy on reasoning tasks, but you're paying N times the tokens and latency — use it where correctness matters more than cost.
Plainly
Think of Self-consistency as a simple recipe for doing the work better. Sampling the model several times on the same prompt and taking the majority answer to beat a single noisy guess.
In practice
Use it when you need a repeatable method instead of guessing from vibes. In practice, define the owner, input, output, and failure mode before you rely on it.