Generative AI Leader Fundamentals of Generative AI Practice Question
A startup is building a customer service chatbot that generates responses in real-time. They want the model to have up-to-date information on the latest product catalog but cannot afford frequent fine-tuning. Which technique should they use to inject current data into the model without retraining?
⚠ Common exam trap
Google Cloud often tests the distinction between in-context learning (via RAG or prompt engineering) and parametric knowledge (via fine-tuning), trapping candidates who think that simply adding more data to the prompt is scalable or that zero-shot inference can substitute for external retrieval.
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
✓
Use retrieval-augmented generation (RAG) to fetch relevant documents from a vector database at inference time.
Retrieval-Augmented Generation (RAG) is the correct technique because it allows the chatbot to fetch the most current product catalog entries from an external vector database at inference time, without requiring any model retraining. This keeps responses grounded in up-to-date information while avoiding the cost and latency of frequent fine-tuning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Rely on the model's zero-shot capabilities to infer product details.
Why it's wrong here
Zero-shot cannot incorporate specific new information.
- ✓
Use retrieval-augmented generation (RAG) to fetch relevant documents from a vector database at inference time.
Why this is correct
RAG enables the model to access external, up-to-date information without retraining.
- ✗
Craft detailed system prompts that include the entire product catalog in the prompt.
Why it's wrong here
Prompt length limits make this impractical for large catalogs.
- ✗
Fine-tune the base model weekly on the latest product catalog.
Why it's wrong here
Fine-tuning is expensive and slow for frequent updates.
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