Generative AI Leader Fundamentals of Generative AI Practice Question
A marketing team wants to generate product descriptions using generative AI. They need to ensure factual accuracy and avoid hallucinations. Which approach should they use?
⚠ Common exam trap
Google Cloud often tests the misconception that detailed prompting alone (Option D) is sufficient to guarantee factual accuracy, when in reality, without external knowledge retrieval, the model can still generate plausible but incorrect information.
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 a retrieval augmented generation (RAG) system that retrieves product facts from a database.
Retrieval Augmented Generation (RAG) is the correct approach because it grounds the model's output in verifiable, external data sources. By retrieving product facts from a database in real-time, the system ensures that the generated descriptions are based on accurate information, directly mitigating the risk of hallucination. This method combines the generative power of an LLM with a retrieval step that provides factual context, making it ideal for applications where precision is critical.
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 code generation model to generate structured descriptions.
Why it's wrong here
Code generation models emit syntactically structured text, not grounded product facts, so hallucinated specifications persist. They suit producing scripts or markup, not factual copy. Grounding generation in retrieved product data constrains output to verifiable source content.
- ✗
Fine-tune the model on all product descriptions using supervised learning.
Why it's wrong here
Fine-tuning on existing product descriptions teaches style and phrasing but cannot guarantee factual accuracy, since the model still generates from learned weights rather than a verified source. Fine-tuning suits tone adaptation; retrieval-augmented generation grounds each claim in current product data.
- ✓
Implement a retrieval augmented generation (RAG) system that retrieves product facts from a database.
Why this is correct
RAG grounds generation in retrieved product facts, so the model conditions its output on authoritative database content rather than parametric memory alone. This directly satisfies the factual accuracy and hallucination-avoidance constraint, since responses are anchored to verifiable source data instead of plausible-sounding invention.
- ✗
Use a large language model with detailed prompt instructions to be accurate.
Why it's wrong here
Prompt instructions shape tone and format but do not supply verified facts, so the model can still hallucinate specifications. Prompting suits flexible, low-cost task steering; factual accuracy requires retrieval-augmented generation, which injects authoritative product data into the context at inference.
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