AI Runbook Staleness Audits: Finding Docs That Lie

End-of-lesson check

10 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-operations-AI-and-runbook-staleness-audit-adults

1. What is the main idea of "AI Runbook Staleness Audits: Finding Docs That Lie"?

   1. Runbooks rot — AI can cross-check docs against actual system behavior and rank which runbooks are most likely to mislead the next on-call engineer.
   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 Runbook Staleness Audits: Finding Docs That Lie"?

   1. doc staleness
   2. runbook rot
   3. on-call risk
   4. doc owner

3. Which use of AI fits this topic best?

   1. Rewrite a runbook for a system whose owner has left the company.
   2. Let the AI decide what matters without your review
   3. Cross-reference runbook commands against current CLI flags and endpoints.
   4. Use the answer before checking whether it fits the situation

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

   1. Cross-reference runbook commands against current CLI flags and endpoints.
   2. Explain the topic in plain language
   3. Organize a draft for human review
   4. Rewrite a runbook for a system whose owner has left the company.

5. What should a careful learner remember about "Runbook staleness ranking"?

   1. Use AI to draft or organize ideas about runbook 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 as a workflow assistant, with human review for decisions that carry risk.
   3. Hide uncertainty so the final answer looks cleaner
   4. Use private or sensitive details before checking permission

7. How should AI output about runbook 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 runbook rot.

9. Which action would help you apply "AI Runbook Staleness Audits: Finding Docs That Lie" responsibly?

   1. Decide which runbooks to retire versus invest in updating.
   2. Use the tool to avoid thinking through the tradeoff
   3. Keep going even if the output conflicts with a trusted source
   4. Rank runbooks by deploy-since-last-review and on-call-page frequency.

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

    1. Decide which runbooks to retire versus invest in updating.
    2. Cross-reference runbook commands against current CLI flags and endpoints.
    3. Ask for a plain-language explanation of doc staleness
    4. Compare the answer with a trusted source