Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
Which TWO techniques can help improve the factual accuracy of a language model's outputs? (Choose two.)
⚠ Common exam trap
Google Cloud often tests the misconception that adjusting decoding parameters (like temperature, top-k, or max tokens) can improve factual accuracy, when in reality these only control output style, length, or randomness, not the correctness of the underlying information.
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 on a domain-specific curated dataset.
Fine-tuning on a domain-specific curated dataset (C) directly adjusts the model's weights using high-quality, verified examples, teaching it to produce factually correct outputs for that domain. This reduces hallucinations by grounding the model in accurate, relevant data rather than relying solely on its pre-training distribution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Decrease the max output tokens.
Why it's wrong here
Shorter outputs don't guarantee accuracy.
- ✗
Increase the temperature parameter.
Why it's wrong here
Higher temperature increases randomness, not accuracy.
- ✓
Fine-tune on a domain-specific curated dataset.
Why this is correct
Fine-tuning adapts the model to domain facts.
- ✓
Implement retrieval-augmented generation (RAG).
Why this is correct
RAG provides factual context from external sources.
- ✗
Use top-k random sampling.
Why it's wrong here
Random sampling does not improve factuality.
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