- A
Use Retrieval-Augmented Generation to retrieve relevant legal texts
RAG grounds the summary in actual documents, reducing hallucination.
- B
Increase temperature to 1.5 during inference
Why wrong: Higher temperature increases randomness, likely harming accuracy.
- C
Implement early stopping during fine-tuning
Why wrong: Early stopping prevents overfitting but does not improve factual accuracy.
- D
Incorporate a human-in-the-loop review process
Human review ensures accuracy and catches hallucinations before delivery.
- E
Use character-level tokenization to improve spelling
Why wrong: Character-level tokenization is not standard for large models and doesn't address summarization accuracy.
Question 66 of 997
Combine RAG and Human-in-the-Loop to Avoid Hallucinations
This Generative AI Leader practice question tests your understanding of techniques to improve generative ai model output. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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.)
Quick Answer
The correct combination is Retrieval-Augmented Generation (RAG) and a human-in-the-loop review process. RAG grounds the model’s output in verified source material, directly reducing hallucinations by forcing the summary to cite retrieved legal documents rather than inventing facts. Adding human-in-the-loop validation then catches any remaining inaccuracies before the final output, creating a safety net for high-stakes legal work. On the Google Cloud Generative AI Leader exam, this question tests your understanding of how to enforce factual accuracy in production systems—a common trap is confusing model training techniques like early stopping or temperature adjustment with inference-time safeguards. Remember the memory tip: “Retrieve, then review” to anchor the two-step defense against hallucinations.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
RAG works by embedding the user query and retrieving top-k relevant document chunks from a vector database (e.g., using cosine similarity on embeddings from a model like Sentence-BERT), then concatenating them with the prompt for the generator. In legal contexts, this retrieval step can be tuned with domain-specific embeddings and metadata filtering (e.g., jurisdiction, date) to ensure only valid precedents are used. A real-world scenario: a model summarizing a contract dispute must retrieve the exact clause language to avoid fabricating terms—RAG with a curated legal corpus achieves this, while a standalone fine-tuned model might hallucinate clauses.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Techniques to Improve Generative AI Model Output — This question tests Techniques to Improve Generative AI Model Output — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this Generative AI Leader question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
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.
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