Generative AI Leader Fundamentals of Generative AI Practice Question
A healthcare company is building a clinical decision support system using Gemini 1.5 Pro on Vertex AI. They need responses that are highly accurate and comply with medical regulations, including traceability to source documents. They have a large corpus of curated medical guidelines stored in PDFs in Cloud Storage. Their team has experience with both fine-tuning and prompt engineering. Which approach best ensures regulatory compliance and accuracy?
⚠ Common exam trap
The Generative AI Leader exam often tests the misconception that fine-tuning is the best way to ensure accuracy and compliance for domain-specific tasks, but the trap here is that fine-tuning sacrifices traceability and can introduce staleness, whereas grounding with system instructions preserves source attribution and regulatory compliance.
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 combination of grounding to the medical guidelines and prompt engineering with system instructions specifying compliance requirements.
Grounding the model to the curated medical guidelines in Cloud Storage ensures responses are directly traceable to source documents, which is critical for medical regulatory compliance. Combining this with system instructions that specify compliance requirements (e.g., HIPAA, FDA guidelines) enforces behavioral constraints without altering the model's weights, maintaining accuracy and auditability.
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 combination of grounding to the medical guidelines and prompt engineering with system instructions specifying compliance requirements.
Why this is correct
Grounding ensures traceability to source documents, and prompt engineering enforces regulatory language, together meeting compliance.
- ✗
Use prompt engineering with system instructions and few-shot examples, but no grounding.
Why it's wrong here
Without grounding, the model may still generate inaccurate information from its internal knowledge.
- ✗
Use grounding to the medical guidelines but rely on prompt engineering only for compliance instructions.
Why it's wrong here
Grounding alone may not prevent the model from generating ungrounded answers if not used with strict mode.
- ✗
Fine-tune the model on the medical guidelines corpus to internalize the knowledge.
Why it's wrong here
Fine-tuning does not inherently provide traceability to specific documents and may struggle with updates.
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