1Z0-1127-25 Fundamentals of Large Language Models Practice Question
A company is building a chatbot using OCI Generative AI service. They want to ensure that the model responses are grounded in their internal knowledge base. Which approach should they use?
⚠ Common exam trap
Many exam-takers confuse fine-tuning (Option B) as the only way to incorporate proprietary data, overlooking that RAG provides a more flexible, cost-effective, and updatable method for grounding responses in a dynamic knowledge base without altering model weights.
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
✓
Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) is the correct approach because it retrieves relevant documents from the company's internal knowledge base at inference time and provides them as context to the LLM, ensuring the model's responses are grounded in verifiable, up-to-date information without modifying the model itself. This directly addresses the requirement to ground responses in an internal knowledge base while avoiding the cost and complexity of 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.
- ✗
Prompt engineering with few-shot examples
Why it's wrong here
Few-shot prompting may help with style but cannot provide comprehensive factual grounding.
- ✗
Fine-tuning the model on the internal knowledge base
Why it's wrong here
Fine-tuning adapts the model's behavior but does not guarantee retrieval of specific facts during inference.
- ✗
Model distillation to compress the knowledge base
Why it's wrong here
Distillation reduces model size but does not incorporate external knowledge retrieval.
- ✓
Retrieval-Augmented Generation (RAG)
Why this is correct
RAG retrieves relevant documents from a knowledge base and uses them to generate grounded responses.
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