Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A development team is integrating a large language model into a healthcare application. They need to reduce the risk of generating harmful medical advice. Which THREE measures should they implement? (Choose three.)
⚠ Common exam trap
The Generative AI Leader exam often tests the misconception that disclaimers or system instructions alone are sufficient safety measures, when in fact they do not technically prevent the model from generating harmful content—only post-hoc filtering or architectural controls like RAG and fine-tuning can reduce the risk at the output level.
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 a safety filter to block outputs containing harmful medical terminology.
Implementing a safety filter that blocks outputs containing harmful medical terminology directly mitigates the risk of generating dangerous advice. This acts as a post-processing guardrail, intercepting model outputs that include terms associated with diagnoses, dosages, or procedures that could lead to patient harm. It is a standard practice in high-stakes domains to layer such filters on top of the generative 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.
- ✓
Use a safety filter to block outputs containing harmful medical terminology.
Why this is correct
Safety filters directly block harmful content at inference time.
- ✓
Implement RAG to retrieve verified medical information from trusted sources.
Why this is correct
RAG grounds responses in authoritative sources, reducing hallucination.
- ✓
Fine-tune the model on a curated dataset of medical textbooks.
Why this is correct
Fine-tuning improves domain accuracy and reduces harmful outputs.
- ✗
Include a disclaimer in the system instruction that the model is not a doctor.
Why it's wrong here
A disclaimer does not prevent the generation of harmful advice.
- ✗
Set the temperature to a very high value to ensure diverse outputs.
Why it's wrong here
High temperature increases randomness, potentially generating harmful content.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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