Generative AI Leader Fundamentals of Generative AI Practice Question
A company wants to use a pre-trained language model for customer support summarization. They need to ensure responses are concise and accurate. Which prompt engineering technique is most effective?
⚠ Common exam trap
Google Cloud often tests the misconception that zero-shot prompting is sufficient for all tasks, but the trap here is that candidates overlook the need for explicit guidance in format-sensitive tasks like summarization, where few-shot examples provide the necessary constraint for consistency.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Few-shot prompting with examples
Few-shot prompting (B) is most effective because it provides the model with a small set of example input-output pairs (e.g., a customer query and its concise summary), which guides the model to produce outputs that match the desired format, length, and accuracy. This technique is particularly useful for summarization tasks where consistency and adherence to a specific style are critical, as it reduces ambiguity without requiring fine-tuning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Zero-shot prompting
Why it's wrong here
Zero-shot prompting supplies no examples or constraints, so output length and factual grounding are left entirely to the model's priors, failing the conciseness and accuracy requirement. It is tempting for quick, low-effort tasks where the instruction is unambiguous and no output format must be enforced.
- ✓
Few-shot prompting with examples
Why this is correct
Few-shot prompting supplies labelled input-output pairs, so the model infers the required summary length and factual style directly from the examples. This steers formatting and conciseness without retraining, satisfying the demand for concise, accurate customer support summaries.
- ✗
Chain-of-thought prompting
Why it's wrong here
Chain-of-thought prompting elicits intermediate reasoning steps, producing longer, more verbose outputs that conflict with the conciseness requirement. It is tempting because it improves accuracy on multi-step arithmetic and logical reasoning tasks, where showing working matters more than brevity.
- ✗
Negative prompting
Why it's wrong here
Negative prompting lists behaviours to avoid but provides no positive specification of concise, accurate output, so length and factual grounding remain uncontrolled. It is tempting for suppressing specific unwanted content such as toxic phrasing, where exclusion rather than format control is the goal.
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