Generative AI Leader Fundamentals of Generative AI Practice Question
A company is deploying a generative AI application that generates medical reports. They need to ensure the output is factual and minimizes hallucinations. Which approach is most effective?
⚠ Common exam trap
Test-takers frequently choose 'Set the temperature to 0.0' because they confuse reducing randomness with eliminating factual errors, but temperature only controls output variability, not the truthfulness of the model's internal knowledge.
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) with a curated knowledge base
Retrieval-Augmented Generation (RAG) is the most effective approach because it grounds the model's output in a curated, authoritative knowledge base of medical data. By retrieving relevant, verified documents at inference time, RAG directly reduces the model's reliance on its parametric memory, which is the primary source of hallucinations in generative AI. This is especially critical in high-stakes domains like medical reporting, where factual accuracy is paramount.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Fine-tune the model with RLHF
Why it's wrong here
RLHF helps alignment but doesn't guarantee factual grounding.
- ✗
Set the temperature to 0.0
Why it's wrong here
Low temperature reduces randomness but doesn't prevent hallucination.
- ✓
Implement retrieval-augmented generation (RAG) with a curated knowledge base
Why this is correct
RAG grounds outputs in retrieved facts, reducing hallucinations.
- ✗
Use prompt engineering to instruct the model to be accurate
Why it's wrong here
Prompt engineering is insufficient for factual accuracy.
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