1Z0-1127-25 Prompt Engineering Practice Question
An application uses an LLM to summarize legal documents. The summaries sometimes include hallucinations (details not in the original text). Which prompt engineering technique is MOST effective at reducing 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
✓
Include the full document text in the prompt and instruct the model to base its summary only on that text
Providing the full document context within the prompt (e.g., using a template that includes the document text) grounds the model's response and reduces the chance it invents details. Few-shot examples can also help but are secondary to providing 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.
- ✗
Increase the temperature to 0.9 to make the model more cautious
Why it's wrong here
Higher temperature increases randomness, which can worsen hallucinations.
- ✗
Use a few-shot prompt with examples of correct summaries
Why it's wrong here
Examples help but may not prevent hallucinations if the model doesn't have the actual document.
- ✓
Include the full document text in the prompt and instruct the model to base its summary only on that text
Why this is correct
Grounding the model with the source text is the most direct way to reduce hallucination.
- ✗
Set the presence penalty to a high value
Why it's wrong here
Presence penalty discourages repetition but does not address factuality.
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