Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
Exhibit
Refer to the exhibit. The following is a partial output from 'gcloud ai models list' command: --- MODEL_ID: 123456789 DISPLAY_NAME: my-summary-model MODEL_REGISTRY: vertex-ai SUPPORT_ENGINE: False GROUNDING_CONFIG: NONE ---
A developer wants to improve the factual accuracy of the model's summaries. Based on the exhibit, what should they do?
⚠ Common exam trap
A common pitfall is mistaking grounding for simply expanding the context window or retraining. Grounding with a knowledge base (RAG) provides direct access to verified facts, which is more effective and efficient than further training or fine-tuning, especially in a production environment.
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
✓
Configure grounding with a knowledge base.
Grounding with a knowledge base is the correct approach because it anchors the model's output to a trusted, external source of facts, directly improving factual accuracy without modifying the model's weights. This technique uses retrieval-augmented generation (RAG) to fetch relevant documents from the knowledge base and inject them into the prompt context, ensuring the summary is based on verified information rather than relying solely on the model's parametric memory.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable the support engine.
Why it's wrong here
The support engine surfaces source citations and grounding documents; it does not alter how the model generates summaries, so factual errors persist. It is tempting because grounding retrieval is the right choice when answers must cite enterprise knowledge sources rather than rely on parametric memory.
- ✗
Increase the model's context window.
Why it's wrong here
A larger context window only admits more input tokens; it does not make the model verify claims against source documents, so inaccuracies remain. It is tempting because expanding context is the right choice when summaries must cover longer documents that currently exceed the token limit.
- ✓
Configure grounding with a knowledge base.
Why this is correct
Grounding with a knowledge base retrieves authoritative source content and supplies it to the model at generation time, so summaries cite retrieved facts rather than relying on parametric memory. This directly reduces hallucination and improves factual accuracy of the summaries.
- ✗
Re-train the model with a dataset of facts.
Why it's wrong here
Retraining is unnecessary and impractical for correcting summarisation grounding; the exhibit points to a retrieval or prompting configuration change instead. It is tempting because fine-tuning on factual data is the correct approach when the base model lacks domain knowledge entirely.
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