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Generative AI Leader Practice Question: A data scientist is evaluating how to ground a…
A data scientist is evaluating how to ground a generative AI model to reduce hallucinations when answering questions about a private knowledge base. Which TWO techniques are most suitable?
⚠ Common exam trap
In the context of the Google Generative AI Leader exam, fine-tuning is often mistakenly thought to be the primary method for grounding a model on private data, when in fact RAG is preferred for dynamic or large knowledge bases because it avoids retraining and allows real-time updates without modifying model weights.
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
✓
Retrieval-Augmented Generation (RAG)
Option D, Retrieval-Augmented Generation (RAG), is correct because it grounds the model by retrieving relevant passages from the private knowledge base at query time and injecting them into the prompt, so answers are based on actual source documents rather than parametric memory, which directly reduces hallucinations. Option E, prompt engineering instructing the model to answer only from the provided context, is correct because it constrains generation to the supplied grounding text and is the standard companion technique used with RAG to keep responses faithful to retrieved content. Option A, using a larger model like Gemini Ultra, does not by itself ground the model in a private knowledge base and can still hallucinate about proprietary data. Option B, fine-tuning on the private knowledge base, can teach style and some facts but is costly, must be redone as data changes, and does not reliably prevent hallucinations or provide citations. Option C, increasing the temperature to 0.9, raises randomness and would make hallucinations more likely, not less.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Using a larger model like Gemini Ultra
Why it's wrong here
A larger model like Gemini Ultra still relies on parametric memory, which contains no private knowledge base content, so it cannot ground answers in that corpus. It is tempting because larger models reason and generalise well, and it would be the correct choice when improving general capability on public-domain tasks.
- ✗
Fine‑tuning on the private knowledge base
Why it's wrong here
Fine-tuning on the private knowledge base bakes facts into weights, which is costly to refresh and still hallucinates; retrieval at inference time grounds answers in current source documents. It is tempting because fine-tuning adapts style and task behaviour, and it would be correct for teaching a model a consistent output format or domain tone.
- ✗
Increasing the temperature to 0.9
Why it's wrong here
Raising temperature to 0.9 increases sampling randomness, producing more varied and less faithful tokens, which worsens hallucination rather than grounding answers in the private knowledge base. It is tempting because temperature tuning controls creativity, and it would be the correct choice when generating diverse brainstorming or creative content.
- ✓
Retrieval-Augmented Generation (RAG)
Why this is correct
Retrieval-Augmented Generation retrieves relevant passages from the private knowledge base at query time and injects them into the model's context, so answers are conditioned on actual source documents rather than parametric memory alone. This directly satisfies the grounding constraint, reducing hallucination without retraining or fine-tuning the underlying model.
- ✓
Prompt engineering to instruct the model to answer based only on the provided context
Why this is correct
Instructing the model to answer solely from supplied context constrains generation to retrieved passages, directly reducing hallucination. This grounding technique satisfies the private knowledge base requirement without retraining, since the prompt supplies authoritative content the model must cite rather than invent.
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