Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A team is fine-tuning a model for a legal document summarization task. They need to ensure high accuracy and avoid hallucinations. Which TWO approaches should they combine? (Choose two.)
⚠ Common exam trap
A common misconception is that increasing temperature or using training-time techniques like early stopping can improve inference accuracy, when in fact they either increase randomness or address overfitting, not factual grounding. This trap is frequently tested in Google certification exams.
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 Retrieval-Augmented Generation to retrieve relevant legal texts
Retrieval-Augmented Generation (RAG) is correct because it grounds the model's output in retrieved, authoritative legal texts, directly reducing hallucination by providing factual context during generation. This is critical for legal summarization where accuracy is paramount, as RAG ensures the model references specific statutes or case law rather than relying solely on its parametric memory.
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 Retrieval-Augmented Generation to retrieve relevant legal texts
Why this is correct
RAG grounds the summary in actual documents, reducing hallucination.
- ✗
Increase temperature to 1.5 during inference
Why it's wrong here
Higher temperature increases randomness, likely harming accuracy.
- ✗
Implement early stopping during fine-tuning
Why it's wrong here
Early stopping prevents overfitting but does not improve factual accuracy.
- ✓
Incorporate a human-in-the-loop review process
Why this is correct
Human review ensures accuracy and catches hallucinations before delivery.
- ✗
Use character-level tokenization to improve spelling
Why it's wrong here
Character-level tokenization is not standard for large models and doesn't address summarization accuracy.
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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.