Generative AI Leader Fundamentals of Generative AI Practice Question
A healthcare company is building a generative AI assistant to answer patient questions about medications. The team wants to ensure the model's responses are grounded in approved clinical guidelines and avoid fabricated information. Which approach best addresses this requirement?
⚠ Common exam trap
The trap here is thinking that fine-tuning alone can eliminate hallucinations, when grounding with retrieval is often necessary for dynamic, factual accuracy.
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) to fetch relevant passages from a curated clinical guideline database before generating a response.
Retrieval-augmented generation (RAG) grounds the model's output by retrieving relevant, up-to-date information from a curated database and conditioning the generation on that context. This reduces hallucinations and ensures responses align with approved clinical guidelines. Other options either increase randomness, rely solely on fine-tuning without retrieval, or incorrectly assume smaller models are safer.
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 retrieval-augmented generation (RAG) to fetch relevant passages from a curated clinical guideline database before generating a response.
Why this is correct
RAG combines a retriever that searches a trusted knowledge base with a generator that conditions its output on the retrieved passages. This grounds responses in approved clinical guidelines and reduces hallucinations. For a healthcare assistant, RAG ensures answers are based on current, authoritative content, directly addressing the requirement for accuracy and safety.
- ✗
Use a smaller model with fewer parameters to minimize the risk of generating incorrect information.
Why it's wrong here
Model size does not guarantee factual accuracy. Smaller models may have less knowledge and still hallucinate. Reducing parameters could degrade performance and coherence without solving grounding. The issue is not model capacity but the lack of a mechanism to reference verified sources. A smaller model alone would not ensure adherence to clinical guidelines.
- ✗
Increase the model's temperature to encourage more creative and varied responses.
Why it's wrong here
Higher temperature increases randomness and creativity, which would likely worsen hallucinations and reduce adherence to guidelines. The goal is factual grounding, not diversity. This option would make the assistant less reliable and potentially unsafe for medical advice. Temperature tuning is not a solution for factual accuracy; it controls style, not truthfulness.
- ✗
Fine-tune the model on a large corpus of general medical textbooks without any retrieval mechanism.
Why it's wrong here
Fine-tuning can improve domain knowledge, but without retrieval, the model may still hallucinate or provide outdated information. Textbooks may not reflect the latest approved guidelines. This approach lacks the dynamic grounding needed for clinical accuracy. Retrieval-augmented generation is more effective for ensuring responses are based on current, authoritative sources.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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