AI Code Review Policies: Where Humans Stay in the Loop

End-of-lesson check

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

1. What is the main idea of "AI Code Review Policies: Where Humans Stay in the Loop"?

   1. AI-augmented code review accelerates teams. The policies around what AI flags vs what humans must review separate good teams from sloppy ones.
   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 Code Review Policies: Where Humans Stay in the Loop"?

   1. human review
   2. code review
   3. AI augmentation
   4. policies

3. Which use of AI fits this topic best?

   1. Substitute AI review for senior engineer judgment on high-stakes changes
   2. Let the AI decide what matters without your review
   3. Use AI for first-pass review (style, common bugs, security patterns)
   4. Use the answer before checking whether it fits the situation

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

   1. Use AI for first-pass review (style, common bugs, security patterns)
   2. Explain the topic in plain language
   3. Organize a draft for human review
   4. Substitute AI review for senior engineer judgment on high-stakes changes

5. What should a careful learner remember about "AI code review policy"?

   1. Use "AI code review policy" as a reminder to verify the AI output before anyone relies on it.
   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 code review 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 code review.

9. Which action would help you apply "AI Code Review Policies: Where Humans Stay in the Loop" responsibly?

   1. Replace the team-conversation aspect of code review
   2. Use the tool to avoid thinking through the tradeoff
   3. Keep going even if the output conflicts with a trusted source
   4. Require human review for: architectural changes, security-sensitive code, novel patterns

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

    1. Replace the team-conversation aspect of code review
    2. Use AI for first-pass review (style, common bugs, security patterns)
    3. Ask for a plain-language explanation of human review
    4. Compare the answer with a trusted source