Generative AI Leader Fundamentals of Generative AI Practice Question
During a RAG pipeline implementation, the retrieval system frequently returns irrelevant documents, causing the generator to produce incorrect answers. Which change is most likely to improve the relevance of retrieved documents?
⚠ Common exam trap
Generative AI Leader often tests the effectiveness of different RAG optimization techniques; candidates may think increasing retrieved documents or changing embedding models is sufficient, but re-ranking is specifically designed to improve relevance.
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 re-ranking step using a cross-encoder model to refine the top results.
Adding a re-ranking step using a cross-encoder model refines the initial retrieval results by scoring each document against the query more accurately, thus improving relevance. Cross-encoders consider the interaction between query and document, unlike bi-encoders used in vector search, leading to better ranking. The other options are less likely to directly improve relevance.
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 re-ranking step using a cross-encoder model to refine the top results.
Why this is correct
A cross-encoder re-ranker scores each query-document pair jointly, capturing semantic interaction that the initial embedding retrieval misses. Re-ordering the top candidates by these scores promotes genuinely relevant documents, directly addressing the irrelevant-retrieval constraint causing incorrect generated answers.
- ✗
Increase the number of documents retrieved from the vector store.
Why it's wrong here
Retrieving more documents widens the candidate pool, so irrelevant chunks still reach the generator and can dilute or contradict the correct context. Raising top-k suits recall-oriented tasks where the answer may sit in a lower-ranked passage. Relevance here needs better ranking or filtering, not a larger result set.
- ✗
Use a different embedding model with higher vector dimension.
Why it's wrong here
Higher vector dimension alone does not guarantee better semantic alignment; relevance depends on whether the embedding model was trained for the domain and query style. Switching embeddings is tempting because embedding quality genuinely drives retrieval, and it is the right move when the current model clearly mismatches the corpus language or terminology.
- ✗
Decrease the chunk size of documents to reduce noise.
Why it's wrong here
Smaller chunks reduce context per passage but can split answers across boundaries, lowering recall and stripping the surrounding detail the generator needs. Chunk-size tuning is tempting because it genuinely balances precision against context, and it is correct when chunks are so large that unrelated content dominates each embedding.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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