Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A support team uses a generative AI assistant to answer customer questions. Agents report that the answers are often too long and include unnecessary background. The team wants the responses to be concise and directly address the question. Which prompt adjustment is most appropriate?
⚠ Common exam trap
Test-takers frequently confuse randomness controls such as temperature and top-p with instruction-following controls such as explicit length and scope constraints.
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
✓
Tell the model to answer in two sentences or fewer and to skip background unless the customer asks for it.
The most appropriate adjustment is an explicit prompt constraint that limits answer length and omits background unless requested. Verbosity is a content and instruction-following issue, so a clear directive in the prompt is the right control. Sampling parameters like temperature and top-p do not directly enforce conciseness, and asking for more detail would worsen the problem.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add a system instruction that the model should always provide a detailed explanation for every answer.
Why it's wrong here
This instruction would make the problem worse by encouraging longer, more detailed responses. The team wants concise answers, so a directive to always explain in detail contradicts the goal. System instructions are powerful, but they must align with the desired behavior. A length constraint is needed instead of a detail mandate.
- ✗
Lower the model's temperature to zero so answers become deterministic.
Why it's wrong here
Temperature affects randomness, not length. A deterministic answer can still be long and include unnecessary background. The agents' complaint is about verbosity and focus, which are governed by prompt instructions. Setting temperature to zero may improve consistency, but it will not make responses concise or directly address the question.
- ✓
Tell the model to answer in two sentences or fewer and to skip background unless the customer asks for it.
Why this is correct
Adding an explicit length and scope constraint directly addresses the verbosity problem. The model receives a clear instruction about how many sentences to use and what to omit. This is a simple prompt-engineering fix that does not require retraining or infrastructure changes. It also preserves the assistant's ability to answer follow-up questions when background is requested.
- ✗
Increase the top-p value so the model considers more possible words.
Why it's wrong here
Top-p controls the breadth of token sampling, not response length or relevance. Increasing it can make output more varied, but it does not tell the model to be concise. The support team needs a directive about length and content, not a change in randomness. This adjustment could even make answers less predictable without solving the verbosity issue.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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