AIF-C01 practice question: Prompt engineering (2 of 4)

Applications of Foundation Models A free question from our original AIF-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.

An accounting firm's model answers multi-step tax calculation questions wrong when asked for the final figure directly, even though the inputs are correct. The firm needs repeatable answers.

Which prompting technique is MOST likely to improve accuracy?

Choose one answer.

A. Ask the model to reason through each step before the final answer
B. Ask for only the final figure, with no working shown in the reply
C. Remove the background figures so the model is not distracted by them
D. Raise the temperature so the model tries more possible answers

Show the answer and explanation

Answer: A

A. Right: Correct. Chain-of-thought prompting asks the model to lay out intermediate steps before answering. For multi-step arithmetic, that reasoning improves accuracy, and the working can be checked. Learn more: Implementing advanced prompt engineering with Amazon Bedrock (opens in a new tab)
B. Wrong: Forcing a bare answer removes the room for intermediate steps, which tends to produce more errors on multi-step problems.
C. Wrong: The needed figures are part of the calculation. Removing them makes correct answers less likely.
D. Wrong: Higher temperature adds randomness, so answers become less consistent. It does not improve step-by-step reasoning, and the firm needs repeatable results.

Explanation

Chain-of-thought prompting guides the model through intermediate reasoning before it gives a final answer. AWS's Bedrock blog on advanced prompt engineering describes this step-by-step approach as improving multistep reasoning. The other options either remove needed information or add randomness.

AIF-C01 practice question: Prompt engineering (2 of 4)

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