- A
Use a longer system prompt without examples
Why wrong: Length alone does not prevent hallucination; constraints are needed.
- B
Use system instructions to constrain the model to only answer from provided context
Correct: This confines the model to the given context, minimizing hallucination.
- C
Set top_p to 0.1
Why wrong: Low top_p limits token choices but does not ground output in facts.
- D
Increase temperature to 0.9
Why wrong: Higher temperature increases randomness, potentially worsening hallucinations.
Question 570 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 company notices that their AI chatbot occasionally generates incorrect information. Which technique can best reduce hallucinations without retraining?
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 system instructions to constrain the model to only answer from provided context
Option B is correct because constraining the model to answer only from provided context directly addresses the root cause of hallucinations—the model generating information not grounded in verified sources. This technique, often implemented via system instructions or retrieval-augmented generation (RAG) pipelines, forces the model to rely on a trusted knowledge base rather than its parametric memory, effectively eliminating unsupported fabrications without requiring retraining.
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 a longer system prompt without examples
Why it's wrong here
Length alone does not prevent hallucination; constraints are needed.
- ✓
Use system instructions to constrain the model to only answer from provided context
Why this is correct
Correct: This confines the model to the given context, minimizing hallucination.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Set top_p to 0.1
Why it's wrong here
Low top_p limits token choices but does not ground output in facts.
- ✗
Increase temperature to 0.9
Why it's wrong here
Higher temperature increases randomness, potentially worsening hallucinations.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the misconception that adjusting sampling parameters (like top_p or temperature) can fix hallucinations, when in reality these parameters control randomness, not factual grounding, and the correct solution is to constrain the model's output to a trusted context.
Trap categories for this question
Command / output trap
Low top_p limits token choices but does not ground output in facts.
Detailed technical explanation
How to think about this question
Under the hood, this technique leverages the model's instruction-following capability to implement a 'closed-book' constraint: the system prompt explicitly instructs the model to base its response solely on a provided context block, often using phrases like 'Answer only using the information in the following text.' In practice, this is combined with RAG where the context is dynamically retrieved from a vector database, and the model's attention is effectively limited to that context, reducing the probability of generating tokens outside the grounded source. A subtle behavior is that even with this constraint, the model may still hallucinate if the context contains contradictions or if the instruction is not strictly followed due to prompt injection or formatting issues.
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 system instructions to constrain the model to only answer from provided context — Option B is correct because constraining the model to answer only from provided context directly addresses the root cause of hallucinations—the model generating information not grounded in verified sources. This technique, often implemented via system instructions or retrieval-augmented generation (RAG) pipelines, forces the model to rely on a trusted knowledge base rather than its parametric memory, effectively eliminating unsupported fabrications without requiring retraining.
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.
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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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