Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A healthcare startup wants to use generative AI to provide clinical decision support. They must minimize the risk of harmful hallucinations. Which business strategy is most appropriate?
⚠ Common exam trap
Google Cloud often tests the misconception that fine-tuning alone is sufficient for domain-specific accuracy, when in fact RAG is superior for reducing hallucinations because it provides dynamic, verifiable grounding rather than static memorization.
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 with meticulously curated medical literature.
Retrieval-augmented generation (RAG) grounds the model's output in a trusted, external knowledge base—here, curated medical literature—which directly reduces the risk of hallucination by forcing the model to cite or derive answers from verified sources. This is the most effective strategy for clinical decision support because it combines generative flexibility with factual accuracy, unlike methods that only limit output or rely on post-hoc filtering.
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 with meticulously curated medical literature.
Why this is correct
Retrieval-augmented generation grounds responses in curated medical literature, so answers cite retrieved evidence rather than relying on parametric memory alone. This directly minimises hallucination risk, the stated constraint for clinical decision support where fabricated content could cause harm.
- ✗
Limit the model's output length to reduce hallucination risk.
Why it's wrong here
Truncating output length only shortens responses; it does not verify clinical claims, so hallucinations persist. It is tempting because shorter answers expose fewer statements to scrutiny, which helps latency or cost control, not factual grounding in decision support.
- ✗
Deploy a large general-purpose model and rely on post-processing filters.
Why it's wrong here
Post-processing filters catch some unsafe text but cannot detect clinically wrong statements, so harmful hallucinations survive. It is tempting because filters are cheap to bolt onto any model, which suits general content moderation rather than grounding clinical decision support in authoritative sources.
- ✗
Use a custom fine-tuned model on a proprietary medical dataset.
Why it's wrong here
Fine-tuning on medical data adapts style and terminology but cannot guarantee factual recall, so hallucinations remain. It is tempting because domain-specific training improves relevance, making it the right choice when the goal is specialised vocabulary rather than verifiable accuracy.
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