Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
A media company uses Vertex AI to generate video captions. The generated captions sometimes contain factual errors about named entities (e.g., actor names). Which technique would most likely reduce these errors?
⚠ Common exam trap
A common mix-up: candidates confuse techniques that control output randomness (temperature, top_p) with techniques that improve factual accuracy, overlooking the fundamental need for external knowledge retrieval via grounding.
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 Vertex AI grounding with a knowledge base of verified entities
Vertex AI grounding connects the model to a knowledge base of verified entities, allowing it to retrieve authoritative facts during generation. This reduces hallucinations about named entities by constraining outputs to validated data rather than relying solely on the model's parametric knowledge.
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 response caching
Why it's wrong here
Caching reuses previous responses, not correcting errors.
- ✗
Increase the temperature parameter
Why it's wrong here
Higher temperature increases randomness, likely worsening errors.
- ✓
Use Vertex AI grounding with a knowledge base of verified entities
Why this is correct
Grounding supplies factual context to the model.
- ✗
Decrease top_p to 0.3
Why it's wrong here
Reduces token variety but doesn't introduce facts.
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