AI-102 Implement generative AI solutions Practice Question
You are using Azure OpenAI Service to generate product descriptions. You notice that the model occasionally outputs descriptions that contain factual inaccuracies about product specifications. You want to reduce these hallucinations without changing the model. What should you do?
⚠ Common exam trap
Candidates often confuse hyperparameter tuning (like temperature or frequency_penalty) with prompt engineering techniques, mistakenly believing that adjusting randomness or repetition penalties can fix factual hallucinations, when only providing the correct context in the prompt can do so without model changes.
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
✓
Provide the product specifications in the prompt and use the system message to instruct the model to base answers on them.
Providing the product specifications directly in the prompt and using the system message to instruct the model to base its answers on them grounds the generation in factual data, reducing hallucinations. This technique, known as 'grounding' or 'retrieval-augmented generation' (RAG), does not modify the model itself but constrains its output to the provided context, which is the only way to reduce factual inaccuracies without changing model parameters.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the frequency_penalty parameter.
Why it's wrong here
Frequency penalty discourages repetition, not factual inaccuracies.
- ✗
Decrease the temperature parameter.
Why it's wrong here
Temperature controls randomness, not factual accuracy; lower temperature makes output more deterministic but does not fix incorrect facts.
- ✗
Increase the max_tokens parameter.
Why it's wrong here
Increasing max tokens allows longer outputs but may increase the chance of hallucinations.
- ✓
Provide the product specifications in the prompt and use the system message to instruct the model to base answers on them.
Why this is correct
Grounding the model with factual data in the prompt reduces hallucinations by providing accurate context.
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