Google CloudTechniques to Improve Gen AI Output

Prompt Engineering & Zero-Shot — Google Gen AI Leader Practice Question

A representative Google Cloud Generative AI Leader (Google Gen AI Leader) exam question on Prompt Engineering & Zero-Shot. Work through it below, then read why each option is right or wrong.

Short answer

The correct answer is C. Few-shot prompting by including several labeled examples in the prompt.

Few-shot prompting provides the model with a small number of labeled examples directly in the prompt, which helps it understand the desired output format and classification scheme. This typically improves accuracy over zero-shot prompting (option A), which relies solely on the model's pre-trained knowledge without examples. Option C controls randomness but does not teach the model the classification categories, and option D only affects response length, not classification quality.

The Question

A product team wants to use a large language model to classify customer support tickets into categories such as "billing," "technical issue," and "feature request." They have limited labeled examples but want to improve classification accuracy beyond a basic prompt. Which prompting technique should they use?

AIncreasing the maximum output token limit
BZero-shot prompting with no examples provided
CFew-shot prompting by including several labeled examples in the promptCorrect
DReducing the temperature parameter to zero

Why C is correct

Few-shot prompting provides the model with a small number of labeled examples directly in the prompt, which helps it understand the desired output format and classification scheme. This typically improves accuracy over zero-shot prompting (option A), which relies solely on the model's pre-trained knowledge without examples. Option C controls randomness but does not teach the model the classification categories, and option D only affects response length, not classification quality.

Why the other options are wrong

Option A: Increasing the maximum output token limit

Option A does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (C) is the only one that fully meets every constraint stated in the question.

Option B: Zero-shot prompting with no examples provided

Option B does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (C) is the only one that fully meets every constraint stated in the question.

Option D: Reducing the temperature parameter to zero

Option D does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (C) is the only one that fully meets every constraint stated in the question.

Key idea: Prompt Engineering & Zero-Shot

Few-shot prompting provides the model with a small number of labeled examples directly in the prompt, which helps it understand the desired output format and classification scheme. This typically improves accuracy over zero-shot prompting (option A), which relies solely on the model's pre-trained knowledge without examples. Option C controls randomness but does not teach the model the classification categories, and option D only affects response length, not classification quality. On the Google Gen AI Leader exam, questions in the "Techniques to Improve Gen AI Output" domain test whether you can map a scenario's constraints to the right choice. Read the requirement carefully, eliminate options that violate any single constraint, and pick the one that satisfies all of them with the least operational overhead.

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