self-consistency

Multi-path reasoning improves accuracy on math problems by sampling multiple reasoning paths and selecting the most consistent final answer through majority voting. This approach is applicable to math problems, logic puzzles, and high-stakes factual queries.

Self-consistency in prompt engineering is an AI pattern that selects the most frequent final answer across multiple paths. It implements a sampling loop to generate diverse outcomes and applies majority voting to determine a reliable result.

Use majority vote consensus for logic puzzles when you need high accuracy and fixed answers. It samples multiple reasoning paths, extracts final answers, and selects the most frequent outcome to increase reliability for high-stakes decisions.

Yes, you can apply chain-of-thought sampling to factual queries requiring high accuracy. The self-consistency pattern samples multiple reasoning paths and uses majority consensus to select the most consistent final answer.

Majority voting for AI reasoning paths is limited to tasks with fixed answers, such as math problems and logic puzzles. It is not suitable for open-ended generation tasks where multiple valid answers exist or where no clear consensus can be reached.