Fine-Tune vs Prompt: When AI Tuning Pays Off

End-of-lesson check

15 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 core idea behind "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. Sora
   3. YouTube's 'recommended for you' row.
   4. 'Design a phone stand shaped like a dinosaur.'

2. Which term best describes a foundational idea in "Fine-Tune vs Prompt: When AI Tuning Pays Off"?

   1. prompt-engineering
   2. fine-tune
   3. tradeoff
   4. Sora

3. A learner studying Fine-Tune vs Prompt: When AI Tuning Pays Off would need to understand which concept?

   1. fine-tune
   2. tradeoff
   3. prompt-engineering
   4. Sora

4. Which of these is directly relevant to Fine-Tune vs Prompt: When AI Tuning Pays Off?

   1. fine-tune
   2. prompt-engineering
   3. Sora
   4. tradeoff

5. Which of the following is a key point about Fine-Tune vs Prompt: When AI Tuning Pays Off?

   1. Lock in a specific output format reliably.
   2. Reduce token usage on repeated structured tasks.
   3. Bake in tone or style for one specific app.
   4. Slightly improve speed and cost for high-volume tasks.

6. Which of these does NOT belong in a discussion of Fine-Tune vs Prompt: When AI Tuning Pays Off?

   1. Reduce token usage on repeated structured tasks.
   2. Bake in tone or style for one specific app.
   3. Sora
   4. Lock in a specific output format reliably.

7. Which statement is accurate regarding Fine-Tune vs Prompt: When AI Tuning Pays Off?

   1. Justify the cost for low-volume use cases.
   2. Sora
   3. Add new factual knowledge effectively (use RAG instead).
   4. YouTube's 'recommended for you' row.

8. What is the key insight about "Fine-tune decision check" in the context of Fine-Tune vs Prompt: When AI Tuning Pays Off?

   1. Sora
   2. YouTube's 'recommended for you' row.
   3. 'Design a phone stand shaped like a dinosaur.'
   4. Before fine-tuning ask: '1) Will this run >100K times? 2) Is prompt + RAG insufficient? 3) Do I have 500+ high-quality e…

9. What is the key insight about "Fine-tunes age fast" in the context of Fine-Tune vs Prompt: When AI Tuning Pays Off?

   1. A fine-tune is locked to one base model version. When the base model improves, your fine-tune may underperform plain pro…
   2. Sora
   3. YouTube's 'recommended for you' row.
   4. 'Design a phone stand shaped like a dinosaur.'

10. Which statement accurately describes an aspect of Fine-Tune vs Prompt: When AI Tuning Pays Off?

    1. Sora
    2. 99% of use cases are solved with prompts and RAG. Fine-tuning is for narrow, repeated tasks where format matters more than content.
    3. YouTube's 'recommended for you' row.
    4. 'Design a phone stand shaped like a dinosaur.'

11. Which best describes the scope of "Fine-Tune vs Prompt: When AI Tuning Pays Off"?

    1. It is unrelated to tools workflows
    2. It applies only to the opposite beginner tier
    3. It focuses on Fine-tuning is rarely the right answer for most teams — here's when it actually is.
    4. It was deprecated in 2024 and no longer relevant

12. Which section heading best belongs in a lesson about Fine-Tune vs Prompt: When AI Tuning Pays Off?

    1. Sora
    2. YouTube's 'recommended for you' row.
    3. 'Design a phone stand shaped like a dinosaur.'
    4. What AI does well here

13. Which section heading best belongs in a lesson about Fine-Tune vs Prompt: When AI Tuning Pays Off?

    1. What AI cannot do
    2. Sora
    3. YouTube's 'recommended for you' row.
    4. 'Design a phone stand shaped like a dinosaur.'

14. Which of the following is a concept covered in Fine-Tune vs Prompt: When AI Tuning Pays Off?

    1. prompt-engineering
    2. fine-tune
    3. tradeoff
    4. Sora

15. Which of the following is a concept covered in Fine-Tune vs Prompt: When AI Tuning Pays Off?

    1. fine-tune
    2. tradeoff
    3. prompt-engineering
    4. Sora