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Generative AI Leader Practice Question: Piloting a GenAI feature for internal knowledge…
A company is piloting a GenAI feature for internal knowledge base search. During the pilot, users report that the AI sometimes gives incorrect answers based on outdated documents. What is the MOST effective way to address this issue?
⚠ Common exam trap
Generative AI Leader often tests the misconception that fine-tuning or prompt engineering can solve stale-data problems — the correct answer is almost always RAG with a maintained index when the issue is outdated source content.
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 Retrieval-Augmented Generation (RAG) with the knowledge base documents indexed in a vector store and ensure the index is updated when documents change
RAG retrieves the most current, relevant documents from a vector store at query time and feeds them to the model, so answers are grounded in up-to-date knowledge base content. Updating the index when documents change ensures the model never relies on stale information. This directly addresses the root cause: outdated source documents.
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 system instruction to the prompt telling the model to only answer if it is confident
Why it's wrong here
Prompt instructions cannot correct stale source documents; the model still retrieves and trusts them. Confidence phrasing merely changes tone, not grounding. Retrieval-augmented generation with a refreshed, curated index addresses the outdated content directly. System instructions suit constraining output format or tone, not fixing knowledge accuracy.
- ✗
Decrease the temperature parameter of the model to 0 to reduce randomness
Why it's wrong here
Temperature controls sampling randomness, not factual grounding; at 0 the model still draws on stale indexed content and returns the same outdated answer deterministically. Temperature tuning suits tasks needing consistent formatting or reduced creative variation, not correcting source-document accuracy.
- ✓
Implement Retrieval-Augmented Generation (RAG) with the knowledge base documents indexed in a vector store and ensure the index is updated when documents change
Why this is correct
RAG grounds responses in the indexed knowledge base at query time, so stale documents are replaced once the vector index refreshes. This directly addresses the outdated-document constraint, unlike fine-tuning, which bakes stale content into model weights and cannot be updated cheaply.
- ✗
Fine-tune the model on the current knowledge base to improve accuracy
Why it's wrong here
Fine-tuning adjusts model weights and behaviour but does not refresh the retrieval corpus, so outdated documents continue feeding incorrect answers. Fine-tuning suits teaching style, tone or task-specific patterns; grounding answers in current source content requires updating or re-indexing the knowledge base instead.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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