Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
Which TWO techniques are most effective for improving factual accuracy in a generative AI model's responses? (Choose two.)
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
✓
Retrieval-Augmented Generation (RAG) with curated datasets.
Grounding and RAG both provide external authoritative sources to enhance factual accuracy. Fine-tuning on general data doesn't guarantee accuracy, and increasing temperature hurts accuracy. Prompt engineering is helpful but not as robust as retrieval-based methods.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Retrieval-Augmented Generation (RAG) with curated datasets.
Why this is correct
RAG retrieves relevant, up-to-date documents to inform responses.
- ✗
Increasing the model's temperature to 1.5.
Why it's wrong here
Higher temperature increases creativity, not accuracy.
- ✓
Grounding with a trusted knowledge base.
Why this is correct
Grounding forces the model to base responses on provided sources.
- ✗
Using longer system prompts with multiple instructions.
Why it's wrong here
System prompts can guide but are less reliable than external knowledge.
- ✗
Fine-tuning on a large corpus of general text.
Why it's wrong here
General text may contain inaccuracies and doesn't target specific facts.
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