Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A team notices the RAG pipeline sometimes retrieves irrelevant documents. Which THREE improvements should they consider? (Choose three.)
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
✓
Add a reranking step
Using a higher quality embedding model improves semantic understanding, adding a reranking step refines results, and reducing the number of retrieved documents reduces noise. Increasing chunk size can dilute relevance, and using exact keyword matching loses semantic context.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Add a reranking step
Why this is correct
Reranks retrieved documents by relevance.
- ✗
Use exact keyword matching instead of embedding similarity
Why it's wrong here
Keyword matching is less contextually aware.
- ✗
Increase chunk size of documents
Why it's wrong here
Larger chunks may contain irrelevant content.
- ✓
Reduce the number of retrieved documents
Why this is correct
Fewer documents reduces chance of irrelevant ones.
- ✓
Use a higher quality embedding model
Why this is correct
Better embeddings improve relevance scores.
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