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 optimises model behaviour against human preference rankings, shaping tone and alignment rather than grounding output in verified source data. Factual accuracy for medical reports requires retrieval-augmented generation, which supplies authoritative content at inference time. RLHF would be the right choice when aligning a general assistant's style and safety responses to human feedback.
- ✗
Set the temperature to 0.0
Why it's wrong here
Temperature 0.0 makes token selection deterministic, picking the highest-probability token, but a model can still confidently produce unsupported statements. It does not supply external verified facts. Determinism suits reproducible classification or extraction tasks, not grounding medical claims. Retrieval-augmented generation is needed to anchor output in authoritative sources.
- ✓
Implement retrieval-augmented generation (RAG) with a curated knowledge base
Why this is correct
RAG grounds generation in retrieved documents from a curated knowledge base, so the model conditions output on verified source content rather than parametric memory alone. This directly reduces hallucination and improves factual accuracy, satisfying the medical report requirement.
- ✗
Use prompt engineering to instruct the model to be accurate
Why it's wrong here
Instructing the model to be accurate changes no underlying knowledge; the model cannot verify claims it was never trained on and may still fabricate plausible detail. Prompt engineering shapes format and tone, not factual grounding. It would be correct for steering output structure or style, whereas retrieval-augmented generation supplies the verified source content.
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