Generative AI Leader Fundamentals of Generative AI Practice Question
A company is designing a prompt engineering strategy for a customer service chatbot using Gemini. Which two practices are recommended for improving response quality? (Choose TWO)
⚠ Common exam trap
Google Cloud often tests the misconception that higher temperature always improves creativity, but in customer service, lower temperature is critical for deterministic, safe responses, and candidates may overlook the role of system instructions in defining behavior.
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
✓
Use chain-of-thought prompting
Chain-of-thought prompting (A) is recommended because it guides the model to reason step-by-step, improving accuracy on complex customer service queries by breaking down multi-step problems. This technique leverages Gemini's ability to follow logical sequences, reducing errors in tasks like troubleshooting or escalation decisions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use chain-of-thought prompting
Why this is correct
Chain-of-thought encourages logical reasoning, improving accuracy.
- ✗
Always provide multiple examples in the prompt
Why it's wrong here
While few-shot learning can help, too many examples may lead to confusion or token limits.
- ✗
Avoid any context in the prompt
Why it's wrong here
Without context, the model lacks guidance and may produce irrelevant responses.
- ✗
Set temperature to 1.0 for maximum creativity
Why it's wrong here
High temperature increases randomness, which may reduce quality for customer service.
- ✓
Include a system instruction to define the role
Why this is correct
System instructions set context and tone, resulting in more consistent responses.
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