Lesson 42 of 1550
Internal-Doc Q&A Bots: Beyond The Wiki Search Box
Most companies have years of policies, runbooks, and onboarding docs nobody can find. A grounded Q&A bot makes that knowledge addressable — when built carefully. A grounded LLM bot reads the question, retrieves the relevant passages, and answers with citations.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1Why wiki search fails
- 2internal Q&A
- 3grounding
- 4retrieval
Concept cluster
Terms to connect while reading
Section 1
Why wiki search fails
Wiki search is keyword-based: you type 'PTO carryover' and get 200 results, half of them outdated. A grounded LLM bot reads the question, retrieves the relevant passages, and answers with citations. The hard part isn't the LLM — it's the document hygiene.
Three things to fix before you ship
- 1Stale docs: any doc older than 12 months gets a freshness banner in the answer
- 2Conflicting docs: when two sources disagree, the bot must say so and link both
- 3Access control: if a user can't read the underlying doc, the bot can't answer from it
Access control is the silent failure mode
If your bot indexes everything in Confluence including HR investigations and exec compensation memos, congratulations — you just built a leak machine. The retrieval layer must respect the user's permissions, not the indexer's.
Key terms in this lesson
The big idea: a Q&A bot inherits the hygiene of its corpus. Fix the docs first, ship the bot second.
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