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
Option A (Use chain-of-thought prompting) is correct because prompting Gemini to reason step by step before producing a final answer improves accuracy and coherence on complex customer service queries, especially those requiring multi-step logic or policy lookups. Option E (Include a system instruction to define the role) is correct because a system instruction sets persistent behavioral context, such as "You are a helpful customer service agent for X company," which anchors tone, scope, and constraints across all turns without repeating them in every user prompt. Option B is not recommended as a blanket rule: few-shot examples can help, but "always" providing multiple examples wastes tokens, increases latency, and can bias or overfit responses when zero-shot or single-example prompting suffices. Option C is wrong because omitting context strips the model of the grounding information needed to answer domain-specific questions accurately. Option D is wrong because temperature 1.0 increases randomness and creativity, which is undesirable for a customer service chatbot where factual, consistent, and deterministic answers are required; lower temperatures (e.g., 0.2-0.4) are typically preferred.
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 prompting improves response quality by having Gemini generate intermediate reasoning steps before the final answer, which raises accuracy on multi-step customer service queries such as troubleshooting or policy interpretation. This directly satisfies the stem's goal of improving response quality within the prompt engineering strategy.
- ✗
Always provide multiple examples in the prompt
Why it's wrong here
Few-shot examples help, but recommending them always bloats prompts, wastes tokens and can bias responses; examples should be added only when they demonstrably improve output. It is tempting because few-shot prompting is a genuine technique, and it would be correct for tasks needing a specific output format.
- ✗
Avoid any context in the prompt
Why it's wrong here
Omitting context strips the grounding the chatbot needs to answer customer queries accurately, so responses become generic or fabricated. Context is what anchors prompts to relevant policy or product detail. It is tempting because minimal prompts reduce token cost, and that approach suits simple classification or formatting tasks where no domain knowledge is required.
- ✗
Set temperature to 1.0 for maximum creativity
Why it's wrong here
Temperature 1.0 maximises sampling randomness, producing varied and unpredictable wording that undermines the consistency a customer service chatbot requires. Lower values keep answers deterministic and on-brand. It is tempting because high temperature genuinely helps brainstorming or creative writing tasks, where diversity of output is the goal rather than factual reliability.
- ✓
Include a system instruction to define the role
Why this is correct
System instructions set persistent behavioural context — role, tone, and boundaries — that Gemini applies across every turn, rather than relying on per-prompt wording. This directly satisfies the stem's goal of improving response quality by anchoring the chatbot's persona and scope before user input arrives, reducing drift in customer service replies.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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