Internal Document RAG: Making the Wiki Actually Useful Again

End-of-lesson check

10 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-operations-RAG-internal-docs-adults

1. What is the main idea of "Internal Document RAG: Making the Wiki Actually Useful Again"?

   1. Most company wikis are graveyards of stale info. AI RAG systems can resurrect them — when paired with content-freshness tracking and source citation.
   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 "Internal Document RAG: Making the Wiki Actually Useful Again"?

   1. internal knowledge
   2. RAG
   3. wiki
   4. freshness

3. Which use of AI fits this topic best?

   1. Substitute for the discipline of keeping docs updated
   2. Let the AI decide what matters without your review
   3. Index internal docs (Confluence, SharePoint, Notion) with consistent chunking and metadata
   4. Use the answer before checking whether it fits the situation

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

   1. Index internal docs (Confluence, SharePoint, Notion) with consistent chunking and metadata
   2. Explain the topic in plain language
   3. Organize a draft for human review
   4. Substitute for the discipline of keeping docs updated

5. What should a careful learner remember about "Internal RAG architecture review"?

   1. Use "Internal RAG architecture review" 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 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 RAG 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 RAG.

9. Which action would help you apply "Internal Document RAG: Making the Wiki Actually Useful Again" responsibly?

   1. Hide the staleness problem (a fancy AI on stale docs serves stale answers fast)
   2. Use the tool to avoid thinking through the tradeoff
   3. Keep going even if the output conflicts with a trusted source
   4. Surface document age and last-edit-by in every retrieval response

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

    1. Hide the staleness problem (a fancy AI on stale docs serves stale answers fast)
    2. Index internal docs (Confluence, SharePoint, Notion) with consistent chunking and metadata
    3. Ask for a plain-language explanation of internal knowledge
    4. Compare the answer with a trusted source