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.
Option B is correct because Retrieval Augmented Generation (RAG) grounds the model's responses in documents retrieved from a trusted knowledge base, so answers are conditioned on verifiable source content rather than the model's parametric memory, which substantially reduces fabricated or unsupported statements. Option D is correct because lowering the temperature parameter (e.g., to 0.1) sharpens the next-token probability distribution, making the model select high-probability, more deterministic tokens instead of sampling low-probability alternatives that often produce invented facts. Option A does not belong because increasing the maximum token count only allows longer outputs; it does not improve factual grounding and can even give hallucinations more room to expand. Option C does not belong because fine-tuning on the application's training data can reinforce patterns and biases in that data and does not guarantee factual accuracy, and may even increase confident hallucination. Option E does not belong because a larger foundation model with more parameters may be more fluent but is not inherently less prone to hallucination, since scale alone does not provide source grounding or reduce sampling randomness.
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
Raising the token ceiling only lengthens output; it does not ground generation in retrieved evidence, so unsupported claims persist. It is tempting because longer answers suit complex questions, and the parameter would be the right lever when responses are being truncated mid-sentence rather than fabricated.
- ✓
Use Retrieval Augmented Generation (RAG) with a trusted knowledge base.
Why this is correct
RAG grounds generation in retrieved passages from a trusted knowledge base, so answers are conditioned on verifiable source text rather than parametric memory alone. This directly reduces fabrication because the model cites retrieved evidence, satisfying the requirement to lower hallucination likelihood in question answering.
- ✗
Fine-tune the model on the training data used for the application.
Why it's wrong here
Fine-tuning on the application's own training data bakes in that corpus's errors and cannot verify facts at inference time. It is tempting because domain fine-tuning genuinely improves tone and task format, and it would be the right choice when the model must adopt a specialised style or vocabulary.
- ✓
Set a lower temperature parameter (e.g., 0.1) to reduce randomness.
Why this is correct
Temperature controls sampling randomness in the output distribution. Lowering it to 0.1 makes the model favour high-probability tokens, reducing creative drift and unsupported claims. This directly curbs hallucination by making responses more deterministic and grounded in the most likely continuation.
- ✗
Use a larger foundation model with more parameters.
Why it's wrong here
Parameter count does not supply factual grounding; larger models still confabulate when the answer is absent from context. It is tempting because scale improves reasoning and fluency, and a larger foundation model would be the right choice when the task demands broader general capability rather than verifiable accuracy.
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