AI coding: large migrations with checkpoint commits

End-of-lesson check

10 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-ai-coding-migrations-with-checkpoints-r7a1-creators

1. What is the main idea of "AI coding: large migrations with checkpoint commits"?

   1. Break a framework or version migration into named checkpoints. Each checkpoint compiles, passes tests, and is committed before the next prompt.
   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 "AI coding: large migrations with checkpoint commits"?

   1. checkpoint commits
   2. migration strategy
   3. incremental change
   4. unrelated shortcut

3. Which use of AI fits this topic best?

   1. Hold a 50-file migration in working memory coherently
   2. Let the AI decide what matters without your review
   3. Apply a single mechanical transform across many files
   4. Use the answer before checking whether it fits the situation

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

   1. Apply a single mechanical transform across many files
   2. Explain the topic in plain language
   3. Organize a draft for human review
   4. Hold a 50-file migration in working memory coherently

5. What should a careful learner remember about "Try this prompt"?

   1. Use AI to draft or organize ideas about migration strategy, 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 migration strategy 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 migration strategy.

9. Which action would help you apply "AI coding: large migrations with checkpoint commits" responsibly?

   1. Decide which behavioral changes are acceptable
   2. Use the tool to avoid thinking through the tradeoff
   3. Keep going even if the output conflicts with a trusted source
   4. Update imports and call sites consistently

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

    1. Decide which behavioral changes are acceptable
    2. Apply a single mechanical transform across many files
    3. Ask for a plain-language explanation of checkpoint commits
    4. Compare the answer with a trusted source