Short definition
We define Cache Debt as the hidden cost created when AI systems keep stale context, bloated prompts, outdated embeddings, old model cache, unused artifacts, and forgotten agent state.
Longer definition
Cache Debt builds when AI memory and generated material accumulate faster than teams review, refresh, or remove them. Each retained item may look harmless. Together, they can raise storage and compute costs, reduce retrieval quality, preserve obsolete behavior, and make systems harder to understand.
In plain English
AI systems remember things. Teams often forget to maintain what those systems remember. The growing gap between retained state and useful state is Cache Debt.
Examples
- Embeddings that still represent deleted or revised source material.
- Old prompt versions that remain active in production workflows.
- Agent logs and tool results with no retention or deletion policy.
- Local model and package caches left behind by AI-assisted builds.
- Saved conversation context that degrades later answers.
Warning signs
- No one can explain which retained state a workflow still needs.
- Prompt behavior differs between environments without a clear version history.
- Vector search returns material that is no longer authoritative.
- Agent workspaces and cache directories grow without review.
- Debugging begins with clearing state because its provenance is unknown.
Related terms
Cache Debt overlaps with technical debt, data drift, context rot, configuration drift, data retention, prompt versioning, and artifact lifecycle management. It focuses specifically on the cost of AI state that persists beyond its useful or trustworthy life.
A cleanup approach
Start with an inventory. Record what is retained, its owner, its source, its last refresh, and the workflow that depends on it. Remove material with no valid dependency, refresh state that remains valuable, and set a review interval for everything that stays.