Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A healthcare startup uses a generative model fine-tuned on general medical literature to provide preliminary diagnostic suggestions from patient text. The model frequently misses rare diseases and sometimes suggests common conditions that are unlikely given the symptoms. The startup has a curated dataset of rare disease case reports and wants to improve the model’s sensitivity to rare conditions without sacrificing overall accuracy. They cannot afford to retrain the entire model from scratch. The model is deployed on Vertex AI Prediction with low latency requirement. Which approach should they take?
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 a human-in-the-loop system: for outputs with low confidence or suspected rare disease, route to a human expert.
Implementing a human-in-the-loop process for rare disease flags combines AI with expert review, catching misses while maintaining speed for common cases. Option A is wrong because prompt engineering alone may not teach the model about rare diseases. Option B is wrong because increasing top-p restricts vocabulary but doesn't inject knowledge. Option C is wrong because fine-tuning again might cause catastrophic forgetting of common conditions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Perform continued fine-tuning on the rare disease dataset using a low learning rate.
Why it's wrong here
This could lead to catastrophic forgetting of common medical knowledge.
- ✗
Add a system prompt instructing the model to consider rare diseases more carefully.
Why it's wrong here
Prompt engineering may not be sufficient; the model lacks knowledge of specific rare diseases.
- ✗
Reduce top-p sampling to focus on high-probability tokens, assuming rare diseases have lower probability.
Why it's wrong here
This would likely further ignore rare diseases.
- ✓
Implement a human-in-the-loop system: for outputs with low confidence or suspected rare disease, route to a human expert.
Why this is correct
Human-in-the-loop catches edge cases without retraining, preserving accuracy for common conditions.
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