Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A team is fine-tuning a large language model for medical advice. Which TWO techniques are most effective for improving the safety and reliability of the model's outputs?
⚠ Common exam trap
Google Cloud often tests the misconception that hyperparameter tuning (temperature, top_p) or data scaling alone can solve safety issues, when in fact alignment techniques like Constitutional AI and RLHF are specifically designed for that purpose.
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
✓
Constitutional AI
Constitutional AI (A) is correct because it embeds a set of ethical principles directly into the model's training process, allowing the model to self-critique and revise its outputs to avoid harmful or unsafe medical advice. This technique proactively enforces safety constraints without requiring extensive human labeling, making it highly effective for high-stakes domains like healthcare.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Constitutional AI
Why this is correct
Constitutional AI uses predefined rules to guide model behavior.
- ✗
Lowering the temperature to 0.0
Why it's wrong here
Low temperature reduces creativity but doesn't ensure safety.
- ✗
Increasing training data size
Why it's wrong here
More data may not improve safety.
- ✗
Increasing top_p to 1.0
Why it's wrong here
High top_p increases randomness, which can harm reliability.
- ✓
Reinforcement learning from human feedback (RLHF)
Why this is correct
RLHF aligns model with human preferences for safety.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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