1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
An enterprise is using OCI Generative AI with a RAG architecture. They observe that the LLM sometimes produces hallucinated answers that are not supported by the retrieved documents. Which strategy is most effective in reducing these hallucinations?
⚠ Common exam trap
Oracle often tests the misconception that increasing context quantity (top-k) or adjusting model parameters like temperature will solve hallucinations, when in fact the most reliable solution is explicit behavioral instruction through system prompts.
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
✓
Provide clear instructions in the system prompt to answer only based on the provided context.
Explicitly instructing the LLM to answer only based on the provided context directly addresses the root cause of hallucinations in a RAG pipeline: the model's tendency to rely on its parametric knowledge rather than the retrieved documents. This system prompt acts as a behavioral constraint, forcing the model to ground its responses in the supplied context, which is the most effective and widely recommended mitigation strategy.
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 parameter to make outputs more focused.
Why it's wrong here
Higher temperature increases randomness, likely producing more hallucinations.
- ✓
Provide clear instructions in the system prompt to answer only based on the provided context.
Why this is correct
Explicit grounding instructions guide the model to stick to retrieved documents, reducing unsupported claims.
- ✗
Use a smaller LLM to reduce model capacity.
Why it's wrong here
Smaller models are more prone to hallucination, not less.
- ✗
Retrieve more chunks (increase top-k) to provide more context.
Why it's wrong here
More chunks can include irrelevant or contradictory information, potentially increasing hallucinations.
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