Title: Quiz 1 — Adapt, Prompt, or Retrieve: The Model Customization Decision
Course: AIINFRA 201: Model Adaptation — Fine-Tuning & Quantization > Section: Week 01 — Adapt, Prompt, or Retrieve: The Model Customization Decision

Q: Fine-tuning should typically be the first approach you try when a model isn't performing as needed.
- True
- False
A: False

Q: What is the canonical order of the model customization decision sequence?
- Fine-tuning and RAG simultaneously, then prompt engineering
A: Fine-tuning and RAG simultaneously, then prompt engineering

Q: Which problem is RAG specifically designed to solve?
- Enforcing a consistent brand tone across all responses
- Injecting fresh, up-to-date knowledge and enabling citations
- Reducing inference latency to near zero
- Making a small model cheaper than a frontier API at any volume
A: Injecting fresh, up-to-date knowledge and enabling citations

Q: Name one specific condition (other than "we want better answers") that would justify choosing fine-tuning over RAG or prompting.
Feedback: Sample/expected answer: Any one of: durable tone/brand voice that must be intrinsic to the model; enforceable structured output that prompting can't reliably guarantee; latency constraints that rule out a retrieval step; high volume making a smaller fine-tuned model dramatically cheaper than frontier API calls.

Q: Research shows that when fine-tuning examples introduce facts the base model didn't already know, this increases the model's hallucination risk.
- True
- False
A: True

Q: A team wants their support bot to always cite the current return policy accurately. What is the most appropriate tool?
- Fine-tuning on old support tickets
- RAG, retrieving the current policy document at query time
- A longer system prompt with no retrieval
- Reducing the model's temperature to 0
A: RAG, retrieving the current policy document at query time

Q: Which statement best describes the relationship between RAG and fine-tuning in most production systems?
- They are mutually exclusive; a team must pick exactly one
- RAG has fully replaced fine-tuning in modern systems
- They are complementary — RAG supplies facts/citations while fine-tuning handles style and behavioral consistency
- Fine-tuning always includes a retrieval step internally
A: They are complementary — RAG supplies facts/citations while fine-tuning handles style and behavioral consistency

Q: In one sentence, explain why fine-tuning is a poor tool for teaching a model brand-new facts.
Feedback: Sample/expected answer: Because LLMs learn new facts slowly and inconsistently during fine-tuning, and introducing unfamiliar facts this way linearly increases the model's hallucination risk rather than reliably teaching the fact.