Question

A financial analytics team uses an LLM to solve multi-step budget calculations. When they use chain-of-thought prompting, the model shows its reasoning, but occasionally a single arithmetic slip early in the chain leads to a wrong final answer. They want to increase the reliability of the final answers without switching models or fine-tuning. Which technique should they apply?

Accepted Answer

Sample multiple independent reasoning chains for the same prompt and select the answer that appears most consistently across them

Answer

Set the temperature to 0 so the model always produces the same single reasoning chain

Answer

Switch to zero-shot prompting to remove the intermediate reasoning steps that introduce errors

Answer

Increase the maximum output token limit so the model has more room to reason