Generative AI Leader Fundamentals of Generative AI Practice Question
A gen AI application produces hallucinations (factually incorrect outputs). Which mitigation strategy is LEAST effective?
⚠ Common exam trap
Google Cloud often tests the misconception that increasing model temperature improves accuracy by making the model 'more confident,' when in reality it increases randomness and hallucination risk.
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
✓
Increasing model temperature
Increasing model temperature makes the model more random and creative, which directly increases the likelihood of hallucinations. It does not constrain or ground the output in factual data, making it the least effective mitigation strategy among the options.
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 prompt templates with constraints
Why it's wrong here
Prompt templates with constraints merely request brevity or citation and can be ignored by the model, leaving hallucinations unaddressed. It is tempting because it is cheap and requires no retrieval or evaluation infrastructure, and is correct for standardising output format, but it provides no factual verification.
- ✗
Using grounding with a knowledge base
Why it's wrong here
Grounding with a knowledge base directly supplies retrieved, verifiable content that constrains the model's output, so it actively reduces hallucination rather than being least effective. It is tempting because grounding is the standard remedy when answers must reflect an authoritative corpus, such as internal policy documents or product manuals.
- ✗
Implementing retrieval-augmented generation
Why it's wrong here
Retrieval-augmented generation directly reduces hallucination by grounding responses in retrieved source documents, so it is among the most effective mitigations, not the least. It is tempting because RAG genuinely addresses knowledge gaps and stale training data, making it the right choice when outputs must cite current, verifiable enterprise content.
- ✓
Increasing model temperature
Why this is correct
Raising temperature increases sampling randomness, which makes outputs more varied and creative, so it amplifies rather than reduces hallucination. Grounding, retrieval and lower temperature all constrain the model; temperature is the one lever that moves in the wrong direction.
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