AIF-C01 Fundamentals of Generative AI Practice Question
Which TWO actions can help reduce the likelihood of hallucinations in a generative AI model used for question answering?
⚠ Common exam trap
AWS often tests the misconception that simply increasing model size or output length improves answer quality, when in fact grounding through RAG and controlling randomness via temperature are the direct mechanisms to reduce hallucinations.
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
✓
Use Retrieval Augmented Generation (RAG) with a trusted knowledge base.
Retrieval Augmented Generation (RAG) grounds the model's responses in a trusted, external knowledge base, providing factual context that reduces the model's reliance on its parametric memory alone. By retrieving relevant documents at inference time, RAG directly mitigates the risk of hallucination, as the model generates answers based on retrieved evidence rather than inventing information.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the maximum token count to allow more complete answers.
Why it's wrong here
Longer outputs can include more hallucinated details.
- ✓
Use Retrieval Augmented Generation (RAG) with a trusted knowledge base.
Why this is correct
Grounding on real documents reduces hallucinations.
- ✗
Fine-tune the model on the training data used for the application.
Why it's wrong here
Fine-tuning on the same data may not help and could cause overfitting.
- ✓
Set a lower temperature parameter (e.g., 0.1) to reduce randomness.
Why this is correct
Lower temperature makes outputs more deterministic and less creative, reducing hallucinations.
- ✗
Use a larger foundation model with more parameters.
Why it's wrong here
Larger models can still hallucinate and may be worse.
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