Techniques to Improve Generative AI Model Output →hardMultiple ChoiceObjective-mapped

Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output

Exhibit

Refer to the exhibit.

```json
{
  "model": "publishers/google/models/chat-bison@001",
  "endpoint": "us-central1-aiplatform.googleapis.com",
  "parameters": {
    "temperature": 0.9,
    "topP": 0.95,
    "maxOutputTokens": 256,
    "groundingConfig": {
      "sources": []
    }
  },
  "deployment": "production"
}
```

The exhibit shows the deployment configuration for a conversational AI model used in a finance application. Users report that responses are creative but often contain factually incorrect financial advice. Which parameter change would most improve factual accuracy?

⚠ Common exam trap

The Generative AI Leader exam often tests the misconception that adjusting sampling parameters (temperature, topP) can fix factual accuracy issues, when in reality those parameters only control output randomness and diversity, not the truthfulness of the underlying knowledge.

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

✓

Add grounding sources, such as "EnterpriseSearch" or "Web"

Adding grounding sources like EnterpriseSearch or Web provides the model with access to authoritative, up-to-date financial data, which directly reduces hallucinations by anchoring responses in verified facts rather than relying solely on the model's parametric knowledge. This is the most effective technique for improving factual accuracy in a domain where correctness is critical.

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 grounding sources, such as "EnterpriseSearch" or "Web"
Why this is correct
Grounding forces the model to base responses on real data, directly improving factual accuracy.
✗
Lower temperature to 0.1
Why it's wrong here
Lower temperature reduces randomness but does not add factual information; model may still hallucinate.
✗
Increase topP to 1.0
Why it's wrong here
Higher topP increases token sampling diversity, potentially worsening factual errors.
✗
Increase maxOutputTokens to 1024
Why it's wrong here
Changing token limit does not affect the factual correctness of content.

Go deeper

Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output

Exhibit

Why each option matters

Option-by-option breakdown

Related to this question