1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
A team is optimizing a RAG pipeline for OCI Generative AI. They observe that the model's responses are verbose and often include irrelevant details from the retrieved chunks, reducing user satisfaction. They have already tuned the prompt template. What is the most effective next step?
⚠ Common exam trap
OCI GenAI exams often test the misconception that adjusting retrieval parameters (threshold or count) is sufficient to fix relevance issues, when in fact a dedicated re-ranking step is needed to refine the quality of the context passed to the generation model.
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 a re-ranking step using a cross-encoder model.
Implementing a re-ranking step with a cross-encoder model directly addresses the problem of verbose and irrelevant responses. Cross-encoders evaluate the query-document pair jointly, producing a fine-grained relevance score that filters out noisy or off-topic chunks before they reach the generation model. This improves the quality of the context provided to the LLM, reducing verbosity and irrelevance without requiring retraining or altering the retrieval threshold.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply instruction tuning on the generation model.
Why it's wrong here
Instruction tuning is resource-intensive and not targeted at the immediate issue.
- ✓
Implement a re-ranking step using a cross-encoder model.
Why this is correct
Re-ranking scores each chunk for relevance to the query, filtering out noise.
- ✗
Reduce the number of retrieved chunks from 5 to 3.
Why it's wrong here
Reducing chunks may help but does not specifically filter out irrelevant ones.
- ✗
Increase the similarity threshold for retrieval from 0.7 to 0.85.
Why it's wrong here
A higher threshold may exclude relevant chunks, reducing recall.
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