Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A team is using a pre-trained language model to summarize legal documents. They find that summaries often miss key dates and parties involved. Which technique would most effectively improve factual accuracy?
⚠ Common exam trap
Google Cloud often tests the misconception that inference-time parameters (temperature, top-p) or prompting strategies can substitute for targeted training, when in fact only fine-tuning with domain-specific annotated data reliably improves factual accuracy for structured entities.
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
✓
Fine-tune the model on a dataset of legal summaries with annotated key entities.
Fine-tuning on a dataset of legal summaries with annotated key entities directly teaches the model to recognize and reproduce critical factual elements like dates and parties. This supervised learning approach adjusts the model's weights to prioritize entity extraction and accurate generation, which is the most effective method for improving factual accuracy in domain-specific tasks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Fine-tune the model on a dataset of legal summaries with annotated key entities.
Why this is correct
Fine-tuning adapts the model to domain-specific requirements, improving factual accuracy.
- ✗
Use top-p sampling with a low p value.
Why it's wrong here
Low top-p narrows candidate pool but doesn't increase focus on specific entities.
- ✗
Increase the temperature parameter.
Why it's wrong here
Higher temperature increases randomness, likely worsening accuracy.
- ✗
Use chain-of-thought prompting.
Why it's wrong here
Chain-of-thought helps reasoning but may not improve extraction of specific facts without fine-tuning.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This Generative AI Leader practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Generative AI Leader exam.