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
Few-shot examples shape output format and tone but supply no mechanism to fetch each product's actual price or brand, so values remain model-generated. Prompt engineering suits tasks where the answer already resides in the prompt or model knowledge, not dynamic record-level grounding.
- ✗
Fine-tune a foundation model on the entire product catalog
Why it's wrong here
Fine-tuning adjusts weights toward catalog patterns but does not guarantee retrieval of exact current prices or brands, and stale training data risks confident errors. It suits teaching a consistent style or domain tone, not enforcing per-record factual accuracy against live structured attributes.
- ✗
Deploy a larger foundation model with more parameters
Why it's wrong here
Parameter count does not bind the model to catalog values, so prices and brands can still be hallucinated. Larger models improve general capability, not grounding. This suits open-ended generation where no authoritative source exists, but here retrieval of structured attributes is required for factual accuracy.
- ✓
Implement Retrieval-Augmented Generation (RAG) with a knowledge base
Why this is correct
RAG grounds generation in retrieved catalogue records, so structured attributes such as price and brand are injected into the prompt rather than recalled from model weights. This satisfies the factual-accuracy constraint by anchoring outputs to source data, while unstructured reviews supply tone and descriptive language.
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