1Z0-1127-25 OCI Generative AI Service Practice Question
A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. 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
✓
Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store
RAG allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Fine-tune a base LLM on the policy documents monthly
Why it's wrong here
Fine-tuning monthly is expensive and time-consuming.
- ✗
Train a custom model from scratch on the policy documents each month
Why it's wrong here
Impractical due to high compute and time requirements.
- ✗
Use a larger foundation model with a longer context window and paste all documents into each prompt
Why it's wrong here
Not scalable; context limits and cost issues.
- ✓
Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store
Why this is correct
RAG retrieves relevant chunks at query time, avoiding retraining.
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