Fine-Tune vs Prompt: When AI Tuning Pays Off

End-of-lesson check

10 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-tools-ai-fine-tune-vs-prompt-r13a2-creators

1. What is the main idea of "Fine-Tune vs Prompt: When AI Tuning Pays Off"?

   1. Fine-tuning is rarely the right answer for most teams — here's when it actually is.
   2. Use AI as the final authority for the whole decision
   3. Avoid checking the answer once it sounds polished
   4. Focus only on speed instead of judgment

2. Which concept is most central to "Fine-Tune vs Prompt: When AI Tuning Pays Off"?

   1. prompt-engineering
   2. fine-tune
   3. tradeoff
   4. unrelated shortcut

3. Which use of AI fits this topic best?

   1. Add new factual knowledge effectively (use RAG instead).
   2. Let the AI decide what matters without your review
   3. Lock in a specific output format reliably.
   4. Use the answer before checking whether it fits the situation

4. Which limitation should you watch for in this topic?

   1. Lock in a specific output format reliably.
   2. Explain the topic in plain language
   3. Organize a draft for human review
   4. Add new factual knowledge effectively (use RAG instead).

5. What should a careful learner remember about "Fine-tune decision check"?

   1. Use AI to draft or organize ideas about fine-tune, then verify before acting.
   2. Skip the context so the tool can guess faster
   3. Treat the output as private even after sharing it online
   4. Use the answer without checking the source

6. You want to use AI after this lesson. What is the safest next step?

   1. Act immediately because the AI answer is written clearly
   2. Use AI for drafting and comparison, but verify before publishing or relying on it.
   3. Hide uncertainty so the final answer looks cleaner
   4. Use private or sensitive details before checking permission

7. How should AI output about fine-tune be treated?

   1. As proof that no other source is needed
   2. As a replacement for context, consent, or expert review
   3. As a draft or helper output that still needs human judgment and verification
   4. As something that becomes correct when it sounds confident

8. Name one way to verify an AI answer about fine-tune.

9. Which action would help you apply "Fine-Tune vs Prompt: When AI Tuning Pays Off" responsibly?

   1. Justify the cost for low-volume use cases.
   2. Use the tool to avoid thinking through the tradeoff
   3. Keep going even if the output conflicts with a trusted source
   4. Reduce token usage on repeated structured tasks.

10. Which choice is a bad use of AI for this lesson?

    1. Justify the cost for low-volume use cases.
    2. Lock in a specific output format reliably.
    3. Ask for a plain-language explanation of prompt-engineering
    4. Compare the answer with a trusted source