Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A team is using a generative AI model to answer customer questions about a complex product. They want to improve the factual accuracy and reduce hallucinations. Which TWO techniques should they apply? (Choose two.)
⚠ Common exam trap
The trap here is thinking that sampling parameters or generic fine-tuning can improve factual accuracy, when only grounding techniques like RAG and grounded prompts address hallucination directly.
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 a grounded prompt that instructs the model to answer only from provided context and to say 'I don't know' if unsure.
Grounding with RAG supplies authoritative context, and a grounded prompt instructs the model to use only that context and to express uncertainty when appropriate. Together they attack hallucinations at the source: the model no longer relies solely on memorized parameters and is discouraged from fabricating. Other techniques like temperature or top-k affect randomness, not factual grounding, and fine-tuning on generic data does not provide the needed product facts.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use a grounded prompt that instructs the model to answer only from provided context and to say 'I don't know' if unsure.
Why this is correct
A grounded prompt explicitly constrains the model to rely on the supplied context and to admit uncertainty when the answer is not present. This reduces hallucinations by discouraging the model from inventing information. It works well with RAG and is a simple, effective prompt engineering technique for factual accuracy.
- ✓
Ground the model with Retrieval-Augmented Generation (RAG) using an authoritative product knowledge base.
Why this is correct
RAG retrieves relevant, up-to-date facts from a trusted knowledge base and includes them in the prompt, so the model's answers are based on verified information rather than parametric memory. This directly reduces hallucinations by anchoring generation in source documents. It also allows the model to cite sources, increasing trust and enabling verification.
- ✗
Fine-tune the model on a small set of generic customer service dialogues.
Why it's wrong here
Fine-tuning on generic dialogues may improve tone or style but does not inject the specific product facts needed for accurate answers. It can even reinforce plausible but incorrect patterns. Without authoritative data, fine-tuning is not a reliable way to reduce hallucinations about a complex product.
- ✗
Reduce the top-k parameter to a very small value.
Why it's wrong here
A very small top-k restricts sampling to only the most likely tokens, which can make output more repetitive and less nuanced but does not ensure factual correctness. It may reduce diversity but not hallucinations, because the model can still confidently generate incorrect facts. This parameter controls sampling breadth, not truthfulness.
- ✗
Increase the temperature to make the model more confident.
Why it's wrong here
Higher temperature increases randomness and makes the model more likely to generate creative but incorrect details. It does not improve factual accuracy; in fact, it often worsens hallucinations. Confidence is not controlled by temperature, and this setting would undermine the goal of reducing fabricated answers.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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