Generative AI Leader Fundamentals of Generative AI Practice Question
A company wants to build a chatbot that answers questions using their internal knowledge base. Which approach is most suitable?
⚠ Common exam trap
The trap is assuming fine-tuning is the default way to add private knowledge; exams test whether you know RAG is preferred for dynamic, factual, source-grounded 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)
RAG retrieves relevant passages from the internal knowledge base at query time and injects them into the model's context, so the chatbot answers with grounded, up-to-date, source-specific information without retraining. It is the standard pattern when the knowledge base changes frequently and citations or freshness matter. Fine-tuning, training from scratch, or zero-shot prompting do not reliably surface private, changing content.
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 Retrieval-Augmented Generation (RAG)
Why this is correct
RAG retrieves relevant context and generates answers, perfect for knowledge base Q&A.
- ✗
Fine-tune a model on the knowledge base
Why it's wrong here
Fine-tuning bakes knowledge into weights, which is costly to refresh and prone to hallucinating stale facts; retrieval-augmented generation fetches current passages at query time instead. It is tempting because fine-tuning shapes tone and task behaviour well, and it would be correct for teaching a model a consistent output style or domain-specific format.
- ✗
Train a new model from scratch
Why it's wrong here
Training from scratch demands enormous data and compute while still lacking a mechanism to cite or update the internal knowledge base at query time; retrieval-augmented generation grounds answers in retrieved passages. It is tempting because it offers full control over architecture, and it would be correct when building a genuinely novel foundation model.
- ✗
Use zero-shot prompting with no context
Why it's wrong here
Zero-shot prompting supplies no retrieved passages, so the model cannot access private knowledge and will answer from pretraining alone, risking fabrication. It is tempting because it needs no infrastructure, and it would be correct for generic tasks where the answer lies in the model's existing pretrained knowledge.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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