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Knowledge Base Automation With AI

TL;DR

• AI can draft KB articles from tickets, product docs, or prompts. It can also produce wrong, outdated, or confusing content.
• Use AI for first drafts and updates. You verify: Is it accurate? Is it current? Can a customer follow it?
• KB quality directly affects ticket volume. Good KB = fewer tickets. Bad KB = more confusion. Own the bar.

Support knowledge bases are critical. Customers search them before opening tickets. Agents use them to respond. AI can generate articles from past tickets, product docs, or simple prompts. That speeds creation and updates. It also introduces errors. Wrong steps, outdated screenshots, missing edge cases. Your job: use AI for productivity, maintain accuracy.

What AI Can Do

Drafting from tickets:

• "We had 50 tickets about X. Generate a KB article." AI can synthesize. You verify accuracy and completeness.
• Good for: new features, common issues, FAQs. Always fact-check against current product.

Updating existing articles:

• "The UI changed. Update this article." AI can draft the rewrite. You verify the new flow, update screenshots.
• Product changes often. KB goes stale. AI helps. You own the final version.

Drafting from product docs:

• "Create a how-to from this doc." AI can simplify. You ensure it matches the actual product and adds support-relevant context.
• Product docs ≠ support docs. Support needs: troubleshooting, common errors, "what do I do when..." Add that.

Multi-language:

• Translate articles. AI can do it. You verify (or have a native speaker verify). Technical terms and UI strings need care.
• Don't publish machine translation without review. Errors in another language are harder to catch.

What AI Misses

Accuracy:

• Steps that don't work. Buttons that moved. Options that changed. AI trains on past data. Product is current. Verify.
• Run through the steps. As a customer would. Fix what's wrong.

Edge cases and gotchas:

• "This works unless you're on the old plan." "Ignore the error if you see X." AI tends toward happy path. You know the footguns. Add them.
• Support-specific knowledge: you have it. AI doesn't. Inject it.

Tone and audience:

• KB should be clear, scannable, empathetic. AI defaults to generic or slightly robotic. Edit for voice.
• Customers are frustrated when they hit the KB. The article should help, not confuse.

Discoverability:

• Right title, right tags, right placement. AI can suggest. You optimize for search. "What would a customer type?" Test it.

The Workflow

1. Draft with AI — From ticket, doc, or prompt. Get a first version.
2. Verify — Run the steps. Check against current product. Add edge cases. Fix tone.
3. Publish — With review. No blind publish. Accuracy is non-negotiable.
4. Maintain — Set a review schedule. Product changes. AI can help with updates. You approve.

Your Role

• Quality owner. KB is a product. You're the editor. AI drafts; you publish. Your name (or your team's) is on it.
• Accuracy enforcer. Wrong KB is worse than no KB. It creates more tickets and more frustration. When in doubt, don't publish.
• Feedback loop. Tickets tell you what's missing or wrong in KB. Use that. Update. AI can help. You prioritize.

Do This Next

1. Generate one KB article with AI — From a ticket or doc. Publish only after you've run through the steps. List what you fixed. That's your process.
2. Audit 5 high-traffic articles — Are they current? Any errors? Update. Use AI to draft updates. Verify before publish.
3. Define a review schedule — Quarterly? Monthly for critical articles? Stick to it. Stale KB is a liability.