Closing Out Stale Feature Flags with an LLM Sweep

End-of-lesson check

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

1. What is the main idea of "Closing Out Stale Feature Flags with an LLM Sweep"?

   1. Using an LLM to find feature flags that are 100% on, 100% off, or unused — and to draft the cleanup PRs.
   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 "Closing Out Stale Feature Flags with an LLM Sweep"?

   1. dead code
   2. feature flags
   3. tech-debt
   4. code-cleanup

3. Which use of AI fits this topic best?

   1. Know whether a flag is being held open intentionally for rollback
   2. Let the AI decide what matters without your review
   3. Identify flags fully ramped or fully off for >30 days
   4. Use the answer before checking whether it fits the situation

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

   1. Identify flags fully ramped or fully off for >30 days
   2. Explain the topic in plain language
   3. Organize a draft for human review
   4. Know whether a flag is being held open intentionally for rollback

5. What should a careful learner remember about "Flag sweep prompt"?

   1. Use AI to draft or organize ideas about feature flags, 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 feature flags 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 feature flags.

9. Which action would help you apply "Closing Out Stale Feature Flags with an LLM Sweep" responsibly?

   1. Tell which flags belong to which team without ownership data
   2. Use the tool to avoid thinking through the tradeoff
   3. Keep going even if the output conflicts with a trusted source
   4. Draft a PR removing the dead branch and the flag definition

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

    1. Tell which flags belong to which team without ownership data
    2. Identify flags fully ramped or fully off for >30 days
    3. Ask for a plain-language explanation of dead code
    4. Compare the answer with a trusted source