- A
Implement a RAG pipeline that retrieves relevant product and promotion data from the knowledge base and injects it into the prompt.
RAG provides current, specific context to the model, directly improving relevance for new topics.
- B
Increase the temperature to encourage the model to generate more diverse answers.
Why wrong: Higher temperature increases randomness, not relevance.
- C
Add additional safety filters to block irrelevant responses.
Why wrong: Filters cannot fix irrelevant content; they only block harmful content.
- D
Fine-tune the model again on a larger dataset that includes recent support tickets.
Why wrong: Re-fine-tuning is time-consuming and doesn't capture real-time promotions.
Question 43 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 large e-commerce company deploys a generative AI chatbot on Vertex AI for customer service. The chatbot is powered by a fine-tuned model on the company's historical support tickets. Despite high accuracy on training topics, the chatbot frequently gives irrelevant or off-topic answers when customers ask about new products or promotions. The company maintains a comprehensive product catalog and a knowledge base of current promotions. The chatbot's prompts include a system instruction to 'Answer based on your knowledge' and no other retrieval mechanism. The response time requirement is under 3 seconds. Which course of action should the team take?
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 a RAG pipeline that retrieves relevant product and promotion data from the knowledge base and injects it into the prompt.
Option A is correct because implementing a RAG (Retrieval-Augmented Generation) pipeline directly addresses the chatbot's inability to answer questions about new products or promotions. By retrieving relevant, up-to-date information from the company's product catalog and knowledge base and injecting it into the prompt, the model gains access to current data beyond its training cutoff. This approach keeps response times under 3 seconds (as retrieval is fast) and avoids the need for costly retraining, while the system instruction 'Answer based on your knowledge' is replaced with grounded context.
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.
- ✓
Implement a RAG pipeline that retrieves relevant product and promotion data from the knowledge base and injects it into the prompt.
Why this is correct
RAG provides current, specific context to the model, directly improving relevance for new topics.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the temperature to encourage the model to generate more diverse answers.
Why it's wrong here
Higher temperature increases randomness, not relevance.
- ✗
Add additional safety filters to block irrelevant responses.
Why it's wrong here
Filters cannot fix irrelevant content; they only block harmful content.
- ✗
Fine-tune the model again on a larger dataset that includes recent support tickets.
Why it's wrong here
Re-fine-tuning is time-consuming and doesn't capture real-time promotions.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the misconception that fine-tuning alone can solve knowledge gaps for dynamic or time-sensitive data, when in reality RAG is the appropriate technique for incorporating external, frequently updated information without retraining.
Detailed technical explanation
How to think about this question
Under the hood, RAG combines a retriever (e.g., using a dense embedding model like BERT or a sparse retriever like BM25) with a generator (the fine-tuned LLM). The retriever encodes the user query and searches a vector database of the product catalog and knowledge base, returning the top-k relevant chunks. These chunks are then concatenated into the prompt, allowing the LLM to ground its response in current data. A subtle behavior is that the retriever's embedding model must be aligned with the LLM's latent space to avoid semantic mismatch; otherwise, retrieved chunks may be irrelevant, leading to hallucination. In practice, response times under 3 seconds are achievable by using approximate nearest neighbor (ANN) search (e.g., ScaNN or HNSW) and caching frequent queries.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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 a RAG pipeline that retrieves relevant product and promotion data from the knowledge base and injects it into the prompt. — Option A is correct because implementing a RAG (Retrieval-Augmented Generation) pipeline directly addresses the chatbot's inability to answer questions about new products or promotions. By retrieving relevant, up-to-date information from the company's product catalog and knowledge base and injecting it into the prompt, the model gains access to current data beyond its training cutoff. This approach keeps response times under 3 seconds (as retrieval is fast) and avoids the need for costly retraining, while the system instruction 'Answer based on your knowledge' is replaced with grounded context.
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
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