- A
Fine-tune the model on the entire database of papers.
Why wrong: Fine-tuning is resource-intensive and may not generalize well.
- B
Use chain-of-thought prompting to reason step-by-step.
Why wrong: Chain-of-thought improves reasoning but does not prevent hallucination without grounding.
- C
Use few-shot prompting with examples of accurate summaries and set temperature=0.0.
Why wrong: Few-shot examples do not guarantee factual accuracy for unseen papers.
- D
Implement RAG to retrieve relevant abstracts and incorporate them into the prompt.
RAG provides direct factual context from the database.
Question 37 of 997
Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
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 research team is using a large language model to analyze medical research papers and generate summaries. They need to minimize hallucinations while retaining key details. They have access to a curated database of paper abstracts. Which approach is best?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
✓
Implement RAG to retrieve relevant abstracts and incorporate them into the prompt.
Option D is correct because Retrieval-Augmented Generation (RAG) directly addresses hallucination by grounding the model's output in a curated database of paper abstracts. By retrieving relevant abstracts and injecting them into the prompt, the model generates summaries based on verified facts rather than relying solely on its parametric knowledge, which is the most effective way to minimize hallucinations while retaining key details.
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.
- ✗
Fine-tune the model on the entire database of papers.
Why it's wrong here
Fine-tuning is resource-intensive and may not generalize well.
- ✗
Use chain-of-thought prompting to reason step-by-step.
Why it's wrong here
Chain-of-thought improves reasoning but does not prevent hallucination without grounding.
- ✗
Use few-shot prompting with examples of accurate summaries and set temperature=0.0.
Why it's wrong here
Few-shot examples do not guarantee factual accuracy for unseen papers.
- ✓
Implement RAG to retrieve relevant abstracts and incorporate them into the prompt.
Why this is correct
RAG provides direct factual context from the database.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many candidates mistakenly think that fine-tuning or low temperature alone can solve hallucination, but the trap here is that without external retrieval (RAG), the model has no mechanism to verify facts against a trusted source, so it will still generate plausible-sounding but incorrect details.
Detailed technical explanation
How to think about this question
RAG works by embedding the curated database into a vector store (e.g., using FAISS or Pinecone) and performing a similarity search (e.g., cosine similarity) to retrieve the top-k abstracts most relevant to the user's query. The retrieved abstracts are then concatenated into the prompt as context, forcing the model to condition its generation on those specific texts, which dramatically reduces hallucination rates in production systems like medical summarization or legal document analysis.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
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: Implement RAG to retrieve relevant abstracts and incorporate them into the prompt. — Option D is correct because Retrieval-Augmented Generation (RAG) directly addresses hallucination by grounding the model's output in a curated database of paper abstracts. By retrieving relevant abstracts and injecting them into the prompt, the model generates summaries based on verified facts rather than relying solely on its parametric knowledge, which is the most effective way to minimize hallucinations while retaining key details.
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.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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