The quality of your AI's answers depends heavily on how you structure your knowledge base. Here are the practices that make the biggest difference.
The most common reason AI assistants underperform isn't the model—it's the knowledge base. Poorly organized, outdated, or incomplete documents lead to poor answers.
Here's how to set your AI up for success.
Before uploading anything, list the top 20 questions your team or customers ask most often. Then find or create documents that answer each one clearly.
This "questions first" approach ensures your knowledge base is immediately useful rather than theoretically comprehensive.
AI retrieval systems work best with well-structured documents:
Avoid walls of unstructured prose—they're harder to chunk and retrieve effectively.
Stale information is worse than no information. An AI confidently citing an outdated policy creates real problems.
Set a regular review schedule for your core documents. Mark time-sensitive content with explicit dates so it's easy to identify what needs updating.
It's tempting to mirror your org chart in your knowledge base. Resist this instinct.
Organize documents by the questions they answer, not by who owns them. A customer asking about billing doesn't care whether the answer lives in Finance or Customer Success.
AI retrieval systems pull individual document chunks, not whole files. Each section of your document should make sense on its own.
Bad: "See above for details." Good: "Our refund policy (updated Jan 2026) allows full refunds within 30 days of purchase."
In AiSU, collections let you control which documents each assistant can access. Use this to:
Tighter scoping means more accurate, relevant answers.
You don't need a perfect knowledge base on day one. Start with 10–20 high-quality documents, deploy your assistant, and measure which questions it can't answer well. Then fill those gaps.
An iterative approach gets you to value faster than trying to boil the ocean upfront.