Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A team is deploying a large language model for legal document summarization. They find the model occasionally omits critical legal clauses. Which improvement technique would be most effective?
⚠ Common exam trap
Many exam-takers confuse sampling parameters (temperature, top_p) with techniques that ensure completeness; candidates might think lowering temperature or increasing top_p would make the model more reliable, but they only affect randomness, not coverage of required content.
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
✓
Design a prompt that explicitly lists required sections
The most effective technique is to design a prompt that explicitly lists the required sections. This is a form of prompt engineering that directly addresses the omission issue by instructing the model to include all specified clauses, leveraging the model's instruction-following capability. It is immediate, low-cost, and does not require retraining or altering sampling parameters. By enumerating the sections, the model is guided to cover each one, reducing the chance of missing critical content.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Design a prompt that explicitly lists required sections
Why this is correct
Explicitly listing the required sections in the prompt constrains the model's output structure, directing attention to mandatory clauses that free-form summarisation tends to drop. This prompt-engineering approach improves recall of critical content without retraining or architectural changes.
- ✗
Increase the top_p value to 1.0
Why it's wrong here
Raising top_p to 1.0 widens nucleus sampling to the full distribution, increasing randomness and making clause omission more likely, not less. It is tempting because higher values appear to give the model more choice, but the fix is retrieval augmentation or constrained prompting to ground summaries in source text.
- ✗
Fine-tune the model on legal summaries
Why it's wrong here
Fine-tuning on legal summaries teaches style and compression, which can reinforce the very omission behaviour observed, and needs labelled examples. It is tempting because fine-tuning genuinely improves domain tone and format; it would be correct for adapting vocabulary and output structure, not for guaranteeing clause coverage.
- ✗
Lower the temperature to 0.1
Why it's wrong here
Temperature controls sampling randomness, not recall of source content; at 0.1 the model still omits clauses because decoding determinism does not improve retrieval or attention over long legal text. Lowering temperature suits tasks needing reproducible, near-greedy outputs, such as structured extraction with a fixed answer set.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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