AI tools: RAG vs fine-tuning — picking the right adaptation

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-tools-rag-vs-fine-tune-decision-r7a1-creators

1. What is the core idea behind "AI tools: RAG vs fine-tuning — picking the right adaptation"?

   1. RAG is for changing facts. Fine-tuning is for changing behavior. Most teams reach for the wrong one first.
   2. image AI
   3. Custom GPT
   4. Spot off-by-one errors and edge cases when asked.

2. Which term best describes a foundational idea in "AI tools: RAG vs fine-tuning — picking the right adaptation"?

   1. fine-tuning
   2. RAG
   3. model adaptation
   4. image AI

3. A learner studying AI tools: RAG vs fine-tuning — picking the right adaptation would need to understand which concept?

   1. RAG
   2. model adaptation
   3. fine-tuning
   4. image AI

4. Which of these is directly relevant to AI tools: RAG vs fine-tuning — picking the right adaptation?

   1. RAG
   2. fine-tuning
   3. image AI
   4. model adaptation

5. Which of the following is a key point about AI tools: RAG vs fine-tuning — picking the right adaptation?

   1. Use retrieved context when it's well-formatted in the prompt
   2. Acquire stylistic and structural patterns from fine-tuning data
   3. Combine both when the use case genuinely needs both
   4. image AI

6. What is one important takeaway from studying AI tools: RAG vs fine-tuning — picking the right adaptation?

   1. Get consistent voice from RAG alone
   2. Get fresh facts from fine-tuning alone
   3. Replace good evals with either technique
   4. image AI

7. What is the key insight about "Try this decision tree" in the context of AI tools: RAG vs fine-tuning — picking the right adaptation?

   1. image AI
   2. Custom GPT
   3. Need fresh or proprietary FACTS the base model doesn't know? -> RAG.
   4. Spot off-by-one errors and edge cases when asked.

8. What is the key insight about "Watch out: fine-tuning baked-in bugs" in the context of AI tools: RAG vs fine-tuning — picking the right adaptation?

   1. image AI
   2. Custom GPT
   3. Spot off-by-one errors and edge cases when asked.
   4. A fine-tune can lock in mistakes from your training data forever.

9. Which statement accurately describes an aspect of AI tools: RAG vs fine-tuning — picking the right adaptation?

   1. Retrieval-augmented generation and fine-tuning solve different problems. RAG injects fresh facts at query time.
   2. image AI
   3. Custom GPT
   4. Spot off-by-one errors and edge cases when asked.

10. Which best describes the scope of "AI tools: RAG vs fine-tuning — picking the right adaptation"?

    1. It is unrelated to tools workflows
    2. It focuses on RAG is for changing facts. Fine-tuning is for changing behavior. Most teams reach for the wrong one
    3. It applies only to the opposite beginner tier
    4. It was deprecated in 2024 and no longer relevant

11. Which section heading best belongs in a lesson about AI tools: RAG vs fine-tuning — picking the right adaptation?

    1. image AI
    2. Custom GPT
    3. What AI does well here
    4. Spot off-by-one errors and edge cases when asked.

12. Which section heading best belongs in a lesson about AI tools: RAG vs fine-tuning — picking the right adaptation?

    1. image AI
    2. Custom GPT
    3. Spot off-by-one errors and edge cases when asked.
    4. What AI cannot do

13. Which of the following is a concept covered in AI tools: RAG vs fine-tuning — picking the right adaptation?

    1. RAG
    2. fine-tuning
    3. model adaptation
    4. image AI

14. Which of the following is a concept covered in AI tools: RAG vs fine-tuning — picking the right adaptation?

    1. RAG
    2. fine-tuning
    3. model adaptation
    4. image AI

15. Which of the following is a concept covered in AI tools: RAG vs fine-tuning — picking the right adaptation?

    1. RAG
    2. fine-tuning
    3. model adaptation
    4. image AI