Self-consistency
Also called self-consistency prompting, majority voting, sampling and voting
Asking the same question several times, or several ways, and trusting the answer that comes up most often.
Self-consistency is a technique for improving reliability by generating several independent answers to the same problem and comparing them. If five runs of a maths problem give 42, 42, 38, 42 and 42, the majority answer, 42, is more likely to be right than any single run. The idea comes from a 2022 research paper that combined it with chain-of-thought prompting.
It works because different reasoning paths make different mistakes, but correct reasoning tends to converge on the same answer. Sampling with some randomness (a temperature above zero) produces varied paths; taking the most common final answer filters out one-off errors.
The same principle helps beyond formal problems. Checking an estimate with two different methods, asking two different models, or rephrasing a question and comparing answers are all practical forms of self-consistency. Disagreement is a useful signal: it tells you where the model is uncertain and where you should check sources yourself.
Example
To estimate a market size, you ask for a bottom-up estimate (customers x price) and a top-down estimate (share of a known larger market). Both land between £180m and £200m, which makes the range more believable than either method alone.
Common mistake
Treating agreement as proof. If a model has learned a wrong fact, it may repeat it consistently in every run. Self-consistency catches random reasoning slips, not shared misconceptions. For factual claims, check a source.