Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A financial analyst is using a large language model to generate executive summaries from lengthy earnings call transcripts. The summaries often miss key financial figures and include irrelevant details. Which technique should be used to improve the relevance and accuracy of the summaries?
⚠ Common exam trap
The trap here is assuming that increasing token limits or temperature will improve summarization, when actually they can degrade quality.
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 few-shot prompting with examples of well-structured summaries.
Few-shot prompting is a powerful technique to guide the model's output by providing examples. In this scenario, showing the model examples of summaries that include key financial figures and exclude irrelevant details helps it learn the desired pattern, thereby improving relevance and accuracy.
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 few-shot prompting with examples of well-structured summaries.
Why this is correct
Few-shot prompting provides the model with examples of desired output, guiding it to focus on relevant information and format. By showing examples that highlight key financial figures and omit irrelevant details, the model can learn to replicate that behavior. This technique is effective for improving relevance and accuracy in summarization tasks.
- ✗
Increase the maximum output token limit.
Why it's wrong here
Increasing the maximum output token limit allows longer responses but does not improve the relevance or accuracy of the content. The model might simply generate more irrelevant details. This does not address the core issue of missing key figures.
- ✗
Fine-tune the model on a large corpus of general text.
Why it's wrong here
Fine-tuning on general text may not help the model focus on financial figures and could introduce unrelated patterns. Fine-tuning on domain-specific, task-specific data would be more effective, but general text is unlikely to improve summarization relevance. Thus, this is not the best approach.
- ✗
Increase the model's temperature setting.
Why it's wrong here
Increasing temperature makes the output more random and creative, which would likely exacerbate the issue of missing key figures and including irrelevant details. For a task requiring precision and relevance, a lower temperature is more appropriate. Thus, this approach would worsen the problem.
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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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