1Z0-1127-25 Fundamentals of Large Language Models Practice Question
A data scientist is using a large language model to summarize customer support tickets. The model occasionally generates summaries that include hallucinated details not present in the original ticket. Which technique would best reduce hallucinations while maintaining summary quality?
⚠ Common exam trap
Oracle often tests the misconception that simply instructing the model to be factual (Option B) or fine-tuning (Option C) can eliminate hallucinations, when in reality grounding via retrieval (RAG) is the only technique that directly supplies external evidence to constrain generation.
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
✓
Implement retrieval-augmented generation (RAG) to ground the model in relevant documents.
Retrieval-Augmented Generation (RAG) reduces hallucinations by grounding the model's output in external, verifiable documents retrieved from a knowledge base. Instead of relying solely on the model's parametric memory, RAG fetches relevant context (e.g., the original ticket) at inference time, ensuring the summary is factually aligned with the source. This maintains summary quality because the model can still generate fluent text while being constrained to the retrieved evidence.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement retrieval-augmented generation (RAG) to ground the model in relevant documents.
Why this is correct
RAG provides factual context, reducing hallucinations.
- ✗
Use a longer system prompt instructing the model to be factual.
Why it's wrong here
Prompt engineering alone is often insufficient to eliminate hallucinations.
- ✗
Fine-tune the model on a large corpus of general text to improve its knowledge.
Why it's wrong here
Fine-tuning on general data does not specifically target hallucination reduction.
- ✗
Increase the temperature parameter to 0.9 to encourage more deterministic outputs.
Why it's wrong here
Increasing temperature increases randomness, which can worsen hallucinations.
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