Question

A financial analyst is using a gen AI model to solve multi-step word problems involving budget allocations and percentage calculations. The model often produces the correct setup but arrives at the wrong final number, jumping directly to an answer without showing intermediate steps. Which prompt engineering technique is most likely to improve the accuracy of these calculations?

Accepted Answer

Chain-of-thought prompting, asking the model to reason through the problem step by step before giving the final answer

Answer

Zero-shot prompting, giving only the problem with no examples or reasoning guidance

Answer

Lowering the temperature setting so the model produces more deterministic output

Answer

Retrieval Augmented Generation to pull the correct answer from an external database