Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A financial services firm uses a generative AI model on Vertex AI to answer employees' HR policy questions. The model sometimes invents policy details. The firm wants answers grounded in the official HR handbook and needs to cite the source section. Which solution should they implement?
⚠ Common exam trap
The trap here is believing that fine-tuning or a larger context window provides reliable grounding and citations, when retrieval is designed specifically for that purpose.
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
✓
Use retrieval-augmented generation with Vertex AI Search over the HR handbook and instruct the model to cite retrieved sections.
Retrieval-augmented generation over the HR handbook supplies authoritative passages and enables citations, keeping answers grounded and current. Fine-tuning embeds knowledge but does not guarantee citation or easy updates. Low temperature does not add missing knowledge, and stuffing the full handbook into the prompt is inefficient and less precise than retrieval.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use retrieval-augmented generation with Vertex AI Search over the HR handbook and instruct the model to cite retrieved sections.
Why this is correct
RAG retrieves the most relevant handbook passages and injects them into the prompt, so answers are based on official text. Instructing the model to cite the retrieved section provides traceability. When the handbook is updated, reindexing the source keeps answers current without retraining. This directly addresses both grounding and citation requirements.
- ✗
Fine-tune the model on the HR handbook text so the knowledge is embedded in the weights.
Why it's wrong here
Fine-tuning can teach style and some knowledge, but it does not reliably ground answers or provide citations to specific sections. The model may still hallucinate or blend policies. It also requires retraining whenever the handbook changes. Citation and grounding are better served by retrieval at inference time.
- ✗
Increase the context window by using a model with a larger token limit and paste the entire handbook into every prompt.
Why it's wrong here
A larger context window can hold more text, but pasting an entire handbook is inefficient, costly, and may exceed limits or dilute relevance. Retrieval selects the most pertinent sections, improving accuracy and reducing token usage. Citation is also harder when the model must sift through an entire document. This approach is impractical for routine queries.
- ✗
Lower the temperature to zero and rely on the model's pretrained knowledge of HR policies.
Why it's wrong here
Lower temperature reduces randomness but does not prevent hallucination when the model lacks specific policy knowledge. The model's pretrained data likely does not include the firm's internal handbook. Without retrieval, the model has no authoritative source to cite. Temperature alone cannot ground answers in a private document.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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