Detecting Comment Rot with an LLM Code Reviewer

End-of-lesson check

15 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 core idea behind "Detecting Comment Rot with an LLM Code Reviewer"?

   1. Use an LLM to flag comments that no longer match the code they describe.
   2. Replace a senior reviewer's design judgment
   3. Guarantee the suggested cause is the real one
   4. Recommend safer alternatives (online schema change tools, multi-step migrations)

2. Which term best describes a foundational idea in "Detecting Comment Rot with an LLM Code Reviewer"?

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

3. A learner studying Detecting Comment Rot with an LLM Code Reviewer would need to understand which concept?

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

4. Which of these is directly relevant to Detecting Comment Rot with an LLM Code Reviewer?

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

5. Which of the following is a key point about Detecting Comment Rot with an LLM Code Reviewer?

   1. Notice a comment that describes the wrong return type or wrong branch
   2. Flag TODOs that reference deleted modules or shipped features
   3. Suggest a corrected comment grounded in the current code
   4. Run as a non-blocking PR check that surfaces a list, not a wall

6. Which of these does NOT belong in a discussion of Detecting Comment Rot with an LLM Code Reviewer?

   1. Flag TODOs that reference deleted modules or shipped features
   2. Replace a senior reviewer's design judgment
   3. Suggest a corrected comment grounded in the current code
   4. Notice a comment that describes the wrong return type or wrong branch

7. Which statement is accurate regarding Detecting Comment Rot with an LLM Code Reviewer?

   1. Verify claims about external systems the code talks to
   2. Distinguish intentional historical notes from rot
   3. Tell whether a TODO is still business-relevant
   4. Replace a senior reviewer's design judgment

8. What is the key insight about "Comment-vs-code prompt" in the context of Detecting Comment Rot with an LLM Code Reviewer?

   1. Replace a senior reviewer's design judgment
   2. Guarantee the suggested cause is the real one
   3. Recommend safer alternatives (online schema change tools, multi-step migrations)
   4. Ask: 'For each comment in this diff, does it accurately describe the adjacent code? Reply: matches / drifted / unclear, …

9. What is the key insight about "Don't auto-rewrite comments" in the context of Detecting Comment Rot with an LLM Code Reviewer?

   1. Suggest, never silently edit. Comments often encode tacit context the LLM has no way to recover.
   2. Replace a senior reviewer's design judgment
   3. Guarantee the suggested cause is the real one
   4. Recommend safer alternatives (online schema change tools, multi-step migrations)

10. Which statement accurately describes an aspect of Detecting Comment Rot with an LLM Code Reviewer?

    1. Replace a senior reviewer's design judgment
    2. Stale comments mislead more than missing ones — an LLM is uniquely good at noticing when prose and code disagree.
    3. Guarantee the suggested cause is the real one
    4. Recommend safer alternatives (online schema change tools, multi-step migrations)

11. Which best describes the scope of "Detecting Comment Rot with an LLM Code Reviewer"?

    1. It is unrelated to ai-coding workflows
    2. It applies only to the opposite beginner tier
    3. It focuses on Use an LLM to flag comments that no longer match the code they describe.
    4. It was deprecated in 2024 and no longer relevant

12. Which section heading best belongs in a lesson about Detecting Comment Rot with an LLM Code Reviewer?

    1. Replace a senior reviewer's design judgment
    2. Guarantee the suggested cause is the real one
    3. Recommend safer alternatives (online schema change tools, multi-step migrations)
    4. What AI does well here

13. Which section heading best belongs in a lesson about Detecting Comment Rot with an LLM Code Reviewer?

    1. What AI cannot do
    2. Replace a senior reviewer's design judgment
    3. Guarantee the suggested cause is the real one
    4. Recommend safer alternatives (online schema change tools, multi-step migrations)

14. Which of the following is a concept covered in Detecting Comment Rot with an LLM Code Reviewer?

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

15. Which of the following is a concept covered in Detecting Comment Rot with an LLM Code Reviewer?

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