Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A company is using a generative AI model to answer customer questions. The answers are often vague and do not use the company's specific terminology. They want to improve the relevance and specificity of the responses. Which technique should they use?
⚠ Common exam trap
The trap here is assuming that fine-tuning is always necessary to inject domain knowledge, when RAG can achieve similar results more quickly and with less data.
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
✓
Using retrieval-augmented generation (RAG)
Retrieval-augmented generation (RAG) enhances a generative model by retrieving relevant information from a knowledge base and including it in the prompt. This grounds the model's responses in factual, company-specific content, improving relevance and ensuring the use of correct terminology. It is efficient because it does not require retraining the model.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Using retrieval-augmented generation (RAG)
Why this is correct
RAG combines a generative model with a retrieval system that fetches relevant documents from a knowledge base. By grounding responses in company-specific documents, the model can produce more accurate and specific answers that use the correct terminology, directly addressing the issue.
- ✗
Increasing the model's temperature
Why it's wrong here
Increasing temperature makes the model more random and creative, which would likely worsen vagueness and reduce specificity. It does not help the model adhere to company terminology; instead, it may introduce more irrelevant or off-topic content.
- ✗
Fine-tuning the model on company documents
Why it's wrong here
Fine-tuning can adapt the model to company terminology, but it requires a large, high-quality dataset and significant computational resources. For improving relevance and specificity without extensive training, prompt engineering or retrieval-augmented generation (RAG) is more efficient and faster to implement.
- ✗
Reducing the max output tokens
Why it's wrong here
Reducing max output tokens shortens the response length but does not improve relevance or specificity. It may even truncate useful information. The core issue is the model's lack of access to company-specific knowledge, which length limits do not solve.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.