Many chains, one vote: pick the most common final answer.
CoT is useful but not always consistent.
If you prompt the same question multiple times, you might get different answers depending on the temperature setting (we covered temperature in LLMs in How LLMs work).
Self-Consistency embraces this variation.
You ask the LLM to generate multiple reasoning paths and then select the most common final answer.
It’s a simple idea: when in doubt, ask the model several times and trust the majority.
This technique often leads to more robust results, especially on ambiguous or complex tasks.
However, it doesn’t evaluate how the reasoning was done—just whether the final answer is consistent across paths.
