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Generative AI Leader Practice Question: A healthcare startup is building a GenAI…
A healthcare startup is building a GenAI application that answers patient queries based on medical literature. They need to ensure factual accuracy and compliance with healthcare regulations. Which TWO strategies should they use? (Choose 2)
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 RAG Engine with a curated medical knowledge base
Grounding with Google Search improves factual accuracy by basing answers on verified search results. A response schema for structured output is not directly about accuracy. RAG with a curated medical knowledge base ensures answers come from trusted sources. Few-shot prompting alone is insufficient. Fine-tuning on medical data is not selected because two correct options are already chosen.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Rely on few-shot prompting with example Q&A pairs
Why it's wrong here
Few-shot examples can guide tone but do not guarantee factual accuracy across all queries.
- ✗
Fine-tune the model on medical literature
Why it's wrong here
Fine-tuning can improve domain knowledge but is expensive and may not be sufficient for regulatory compliance; RAG and grounding are more direct.
- ✗
Implement a response schema for structured JSON output
Why it's wrong here
Structured output helps parsing but does not improve factual accuracy.
- ✓
Use RAG Engine with a curated medical knowledge base
Why this is correct
RAG retrieves answers from a controlled set of medical documents, ensuring sources are authoritative and up-to-date.
- ✓
Use Grounding with Google Search to verify facts
Why this is correct
Grounding connects the model to Google Search to fact-check outputs, reducing hallucinations.
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