How to Keep an AI Knowledge Base From Going Stale
To keep an AI knowledge base from going stale, treat it as a living system: log every low-confidence retrieval as a gap, auto-draft candidate articles from resolved tickets, and require a human to approve any draft before it becomes groundable content. The one rule that cannot be skipped is the human approval gate, because auto-publishing AI-written articles lets a single hallucinated resolution become a cited source that spreads the error.
The self-improving loop
- Detect gaps: when retrieval is weak, log it as a missing-knowledge signal instead of guessing.
- Draft from resolutions: turn a resolved ticket into a candidate article automatically.
- Gate with a human: a person approves before a draft becomes groundable content.
- Close the loop: a "did this help?" signal shows which articles actually resolve.
Why the human gate is non-negotiable
If AI-drafted articles publish themselves, a wrong resolution can become a trusted source, and every future ticket on that topic inherits the error with full confidence. The approval gate is the firewall: it lets the knowledge base grow from real resolutions while ensuring a hallucination never quietly becomes canon. Freshness without a gate is just faster decay.
How Resolve keeps it fresh
Resolve builds the self-improving loop in. Low retrieval logs a knowledge base gap, resolved tickets auto-draft articles, and a human approves before any draft is groundable. Auto-written articles are badged so their origin is visible, and a "did this help?" signal closes the loop. In Reva, "Train Reva on this" turns a good resolution into an article so the next similar ticket resolves on its own.