Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A team wants to reduce hallucinations in a question-answering model. Which THREE techniques should they consider?
⚠ Common exam trap
Google Cloud often tests the misconception that reducing output length or increasing randomness (temperature) can improve factual accuracy, when in reality these parameters control style and creativity, not truthfulness.
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
✓
Fine-tune the model on a curated factual dataset
Fine-tuning on a curated factual dataset directly adjusts the model's weights to prioritize accurate, domain-specific knowledge, reducing the likelihood of generating unsupported or hallucinated content. This technique anchors the model's output in verified data, making it more reliable for question-answering tasks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Fine-tune the model on a curated factual dataset
Why this is correct
Fine-tuning on factual data improves accuracy.
- ✓
Use retrieval-augmented generation (RAG)
Why this is correct
RAG grounds answers in retrieved documents.
- ✓
Apply prompt engineering with specific instructions to cite sources
Why this is correct
Prompts can encourage factual responses.
- ✗
Reduce the number of tokens in output
Why it's wrong here
Shorter answers may still be hallucinated.
- ✗
Increase the temperature parameter
Why it's wrong here
Higher temperature increases variability and hallucinations.
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