Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A healthcare company is using a generative AI model to produce patient education materials. They want to ensure the output is accurate, avoids harmful advice, and adheres to medical guidelines. Which TWO techniques should they implement to improve the safety and reliability of the model's output? (Choose two.)
⚠ Common exam trap
The trap here is thinking that fine-tuning alone can ensure safety, when in fact it requires curated data and additional safeguards.
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 retrieval-augmented generation (RAG) using a curated medical knowledge base.
Retrieval-augmented generation grounds the model in a trusted medical knowledge base, ensuring accuracy and up-to-date information. Safety filters screen out harmful content, adding a protective layer. Together, these techniques enhance both the reliability and safety of patient education materials.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the maximum output token limit to shorten responses.
Why it's wrong here
Limiting output length does not inherently improve accuracy or safety. Shorter responses might omit important context or warnings, potentially making the content less safe. This technique does not address the core issues of accuracy and adherence to guidelines.
- ✗
Increase the model's temperature to encourage more diverse responses.
Why it's wrong here
Increasing temperature introduces randomness and creativity, which is counterproductive for medical content where accuracy and safety are paramount. It could lead to more varied but potentially harmful or inaccurate advice. Thus, it does not improve safety.
- ✓
Implement retrieval-augmented generation (RAG) using a curated medical knowledge base.
Why this is correct
RAG enhances the model's output by retrieving relevant, up-to-date information from a trusted knowledge base. This grounds the generation in accurate medical facts and guidelines, reducing hallucinations and ensuring adherence to current standards. It directly improves accuracy and safety for patient education materials.
- ✗
Fine-tune the model on a dataset of patient education materials without any safety annotations.
Why it's wrong here
Fine-tuning on unannotated data may not address safety concerns and could even reinforce biases or unsafe patterns present in the data. Without explicit safety annotations, the model may not learn to avoid harmful advice. Therefore, this approach is insufficient for improving safety.
- ✓
Use a safety filter or content moderation API to screen generated text.
Why this is correct
Safety filters and moderation APIs can detect and block harmful, inappropriate, or unsafe content. By screening the model's output, the company can prevent the dissemination of dangerous medical advice. This adds a layer of protection and ensures compliance with safety standards.
Go deeper
Related to this question
About these practice questions
Courseiva writes every Generative AI Leader question from scratch — 1,008 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
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.