Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A team is using a generative AI model to create summaries of customer feedback. The summaries are often too long and include unnecessary details. The team wants to make the summaries more concise without losing key information. Which technique should they use?
⚠ Common exam trap
Test-takers frequently confuse parameters that control randomness or diversity with those that control length, when in fact length is best managed through explicit instructions in the prompt.
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 prompt engineering to specify a maximum word count for the summaries.
Prompt engineering with a specified word limit is a straightforward way to control the length of generated summaries. By instructing the model to be concise and setting a maximum word count, the team can achieve shorter outputs that still capture essential information. This approach is flexible and can be adjusted easily without model retraining.
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 prompt engineering to specify a maximum word count for the summaries.
Why this is correct
Prompt engineering allows the team to include explicit instructions such as 'Summarize in under 50 words.' This directly guides the model to produce concise outputs. It is a simple and effective way to control length without retraining or adjusting technical parameters.
- ✗
Increase the temperature parameter to encourage brevity.
Why it's wrong here
Temperature controls randomness, not length. Increasing it may make outputs more varied but not necessarily shorter. In fact, it could lead to more verbose or off-topic responses. The team needs a method specifically aimed at controlling output length.
- ✗
Reduce the model's top-k parameter to limit vocabulary diversity.
Why it's wrong here
Top-k affects the diversity of word choices, not the overall length of the output. Limiting vocabulary might make the text more repetitive but won't necessarily shorten it. The team needs a technique that directly targets output length, such as explicit instructions.
- ✗
Fine-tune the model on a dataset of short summaries.
Why it's wrong here
Fine-tuning could teach the model to generate shorter summaries, but it requires a labeled dataset and training time. For a quick improvement, prompt engineering is more efficient. Fine-tuning might be overkill if the only issue is length, which can be addressed via instructions.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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