Generative AI Leader Fundamentals of Generative AI Practice Question
A healthcare company is using a generative AI model to summarize patient notes. The model occasionally includes hallucinated medical details. Which strategy best reduces hallucinations?
⚠ Common exam trap
The trap here is assuming that a larger or fine-tuned model will automatically be more factual, when the key is to constrain generation to the provided source text.
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
✓
Provide the model with the patient notes as context and instruct it to only use that information.
Grounding the model by providing the patient notes as context and instructing it to only use that information is the most effective way to reduce hallucinations. This approach, often called retrieval-augmented generation or prompt grounding, forces the model to base its summary on the given text rather than relying on its parametric memory. It also allows for verification against the source.
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 larger model with more parameters.
Why it's wrong here
Larger models may be more fluent but are not immune to hallucinations. They can still generate plausible but incorrect details. Model size alone does not ensure faithfulness to the input. Grounding techniques are needed to tie outputs to the provided notes.
- ✗
Fine-tune the model on a large corpus of general medical literature.
Why it's wrong here
Fine-tuning on general literature might improve domain knowledge but does not prevent hallucination when summarizing specific patient notes. The model could still invent details not present in the notes. The issue is faithfulness to the source, which requires grounding, not broader training.
- ✗
Increase the model's temperature to encourage more diverse outputs.
Why it's wrong here
Higher temperature increases randomness, which would likely increase hallucinations rather than reduce them. The goal is factual accuracy, not diversity. This approach would exacerbate the problem. Temperature should be lowered, not raised, for tasks requiring precision.
- ✓
Provide the model with the patient notes as context and instruct it to only use that information.
Why this is correct
Grounding the model in the source text and explicitly instructing it to rely only on that context reduces hallucinations because the model can copy or paraphrase from the provided notes. This is a form of retrieval-augmented generation or prompt grounding. It constrains the model's output to the given facts.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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