Retrieval Decay Rate
Retrieval Decay Rate is the speed at which a knowledge base stops matching reality once the tools it describes change. Agencies feel this as rework: a new hire follows a two-quarter-old onboarding doc, a strategist quotes a retired rate card, an account lead rebuilds a deliverable that already existed. The framework says the maintenance cadence, not the authoring volume, sets the ceiling on KM value. Slite attacks decay by monitoring connected tools like Slack, GitHub, and Linear and routing suggested fixes to an owner for review, while Guru auto-verifies content as usage rises. The pressure is real: 83% of B2C marketing decision makers already work with AI agents, so clients expect cited, current answers rather than a wiki nobody trusts. Measure decay as the share of pages untouched in 90 days that still get read, then assign an owner per page.
By InnovaAI ResearchPublished Updated
What is Retrieval Decay Rate?
“Stale docs → wrong answers → client rework”
Retrieval Decay Rate is the speed at which a knowledge base stops matching reality once the tools it describes change. Agencies feel this as rework: a new hire follows a two-quarter-old onboarding doc, a strategist quotes a retired rate card, an account lead rebuilds a deliverable that already existed. The framework says the maintenance cadence, not the authoring volume, sets the ceiling on KM value. Slite attacks decay by monitoring connected tools like Slack, GitHub, and Linear and routing suggested fixes to an owner for review, while Guru auto-verifies content as usage rises. The pressure is real: 83% of B2C marketing decision makers already work with AI agents, so clients expect cited, current answers rather than a wiki nobody trusts. Measure decay as the share of pages untouched in 90 days that still get read, then assign an owner per page.