hardMultiple ChoiceObjective-mapped
AIF-C01 Practice Question: A company uses an LLM to summarize medical…
A company uses an LLM to summarize medical research papers. They are concerned about hallucinations. Which combination of techniques would most effectively reduce hallucinations in this context?
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
✓
Retrieval-Augmented Generation (RAG) and Bedrock Guardrails
Retrieval-Augmented Generation (RAG) grounds the model in retrieved documents, and Bedrock Guardrails can enforce content policies and factuality checks, together reducing hallucinations.
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 temperature and top-p sampling parameters
Why it's wrong here
Increasing randomness parameters makes outputs more diverse but can increase hallucinations.
- ✗
Use a smaller model with less capacity
Why it's wrong here
Smaller models may have fewer parameters but still hallucinate and lack the capability to summarize complex medical texts accurately.
- ✗
Few-shot prompting and fine-tuning on more data
Why it's wrong here
Few-shot prompting and fine-tuning may improve performance but do not directly prevent hallucinations by grounding the model in external sources.
- ✓
Retrieval-Augmented Generation (RAG) and Bedrock Guardrails
Why this is correct
RAG retrieves relevant documents to ground the model, and Bedrock Guardrails can block non-factual or harmful outputs, effectively reducing hallucinations.
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