Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A global retailer wants to deploy a generative AI assistant that answers employee questions about HR policies in English, Spanish, and Japanese. The HR policy documents are updated monthly, and the company wants to avoid retraining the underlying large language model. Which approach should the retailer use?
⚠ Common exam trap
The trap here is assuming that fine-tuning is required whenever a generative AI application must reflect organization-specific knowledge, when retrieval is the appropriate pattern for changing factual 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
✓
Use retrieval-augmented generation with a vector index of the current HR policy documents and a foundation model.
Retrieval-augmented generation keeps the language model unchanged while supplying current, relevant HR policy passages at query time. That matches the need to avoid retraining, support multiple languages through prompting, and reflect monthly document updates after re-indexing. Fine-tuning, temperature changes, and full-corpus prompting either add recurring training work or fail to guarantee fresh, grounded answers.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Prepend the entire HR policy corpus to every prompt and rely on the model's context window for accuracy.
Why it's wrong here
Stuffing the full corpus into each prompt is expensive, may exceed context limits, and degrades answer quality when irrelevant passages dilute the prompt. It also does not scale as policies grow, and there is no efficient way to ensure the most relevant section is used. Retrieval selects only the most pertinent passages for each question.
- ✗
Fine-tune a foundation model on the translated HR policy documents each month and deploy the tuned model.
Why it's wrong here
Fine-tuning updates model weights and is generally used to teach style, format, or domain behavior rather than to serve frequently changing factual content. Re-running fine-tuning every month adds cost, latency, and governance overhead, and it makes it harder to prove which policy version generated an answer. For monthly policy refreshes, retrieval of current documents is more appropriate.
- ✗
Increase the model's temperature setting so it can infer policy changes from patterns in previous answers.
Why it's wrong here
Temperature controls randomness in token selection, not access to new facts. Raising it makes output less deterministic and more likely to invent policy details, which is dangerous for HR guidance. It cannot retrieve updated documents or verify that an answer matches the latest policy, so it does not solve the monthly refresh requirement.
- ✓
Use retrieval-augmented generation with a vector index of the current HR policy documents and a foundation model.
Why this is correct
Retrieval-augmented generation grounds responses in documents fetched at query time, so monthly policy changes are reflected as soon as the index is refreshed, without retraining the model. It also supports multilingual answers when the model is instructed to respond in the user's language, and it makes source attribution easier for HR compliance reviews.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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