AIF-C01 Fundamentals of Generative AI Practice Question
A retail company wants to generate product descriptions from catalog data. The data includes structured attributes (e.g., price, brand) and unstructured reviews. The team needs to ensure factual accuracy. Which approach is most appropriate?
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
✓
Implement Retrieval-Augmented Generation (RAG) with a knowledge base
Retrieval-Augmented Generation (RAG) retrieves relevant documents (product attributes, reviews) and provides them as context to the model, reducing hallucinations and grounding responses in 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 prompt engineering with few-shot examples
Why it's wrong here
Prompt engineering alone may not provide sufficient grounding for factual accuracy, especially with complex structured data.
- ✗
Fine-tune a foundation model on the entire product catalog
Why it's wrong here
Fine-tuning may cause the model to memorize training data but does not guarantee up-to-date factual accuracy for dynamic data.
- ✗
Deploy a larger foundation model with more parameters
Why it's wrong here
Larger models do not inherently guarantee factual accuracy; they may still hallucinate without external grounding.
- ✓
Implement Retrieval-Augmented Generation (RAG) with a knowledge base
Why this is correct
RAG retrieves relevant product data at inference time, ensuring factual accuracy and allowing updates without retraining.
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