Detecting Comment Rot with an LLM Code Reviewer

End-of-lesson check

10 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-ai-coding-LLM-code-comment-rot-creators

1. What is the main idea of "Detecting Comment Rot with an LLM Code Reviewer"?

   1. Use an LLM to flag comments that no longer match the code they describe.
   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 "Detecting Comment Rot with an LLM Code Reviewer"?

   1. documentation-drift
   2. comment-rot
   3. code-review
   4. static-analysis

3. Which use of AI fits this topic best?

   1. Tell whether a source-checked note is still business-relevant
   2. Let the AI decide what matters without your review
   3. Notice a comment that describes the wrong return type or wrong branch
   4. Use the answer before checking whether it fits the situation

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

   1. Notice a comment that describes the wrong return type or wrong branch
   2. Explain the topic in plain language
   3. Organize a draft for human review
   4. Tell whether a source-checked note is still business-relevant

5. What should a careful learner remember about "Comment-vs-code prompt"?

   1. Use AI to draft or organize ideas about comment-rot, 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 comment-rot 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 comment-rot.

9. Which action would help you apply "Detecting Comment Rot with an LLM Code Reviewer" responsibly?

   1. Verify claims about external systems the code talks to
   2. Use the tool to avoid thinking through the tradeoff
   3. Keep going even if the output conflicts with a trusted source
   4. Flag TODOs that reference deleted modules or shipped features

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

    1. Verify claims about external systems the code talks to
    2. Notice a comment that describes the wrong return type or wrong branch
    3. Ask for a plain-language explanation of documentation-drift
    4. Compare the answer with a trusted source