Why this site exists
AI systems preserve context, prompts, embeddings, model files, logs, and generated artifacts. Teams benefit from that memory while it stays current. When maintenance falls behind retention, the same material becomes a source of cost and uncertainty.
We define Cache Debt as a practical AI operations term for that gap.
What the term is for
The term gives engineering, platform, security, and operations teams a shared way to ask concrete questions:
- What does this system retain?
- Who owns the retained state?
- When was it last reviewed or refreshed?
- What depends on it now?
- When should it be removed?
What we are not claiming
CachedDebt.com does not present the term as an official framework or an established industry standard. The aim is more modest: describe a recurring maintenance pattern clearly enough that teams can identify it, discuss it, and decide what to do next.
Start small
Choose one AI workflow. Inventory its retained context and artifacts. Find what is current, what is stale, and what has no clear owner. That is enough to begin.