CachedDebtRead the definition
Field note 001Defining the term

Your AI stack is carrying Cache Debt.

Find stale context, bloated prompts, forgotten embeddings, old agent artifacts, and model cache waste before they slow everything down.

Read the definitionView the checklist
Cache Debt LedgerAn audit map connecting active context to prompt versions, embeddings, agent artifacts, and model cache, with retained, stale, orphaned, and reviewed state.01 / INSTRUCTIONPrompt VersionsRevision trail incomplete02 / RETRIEVALEmbeddingsSource age unresolved03 / EXECUTIONAgent ArtifactsOwnership not assigned04 / STORAGEModel CacheResidue review pendingCURRENT LAYERActive ContextDRIFT RISKREVIEW DUERETENTION UNKNOWNRETENTION LEDGERRETAINEDSTALEORPHANEDREVIEWEDSTATE AUDIT / RETENTION CLASSIFICATION

AI operations language / v0.1

The definition

AI systems remember more than teams maintain.

We define Cache Debt as the hidden cost created when AI systems keep stale context, bloated prompts, outdated embeddings, old model cache, unused artifacts, and forgotten agent state.

It accumulates quietly when retained material grows faster than a team can review, refresh, or remove it.

Read the complete definition

Why it matters

Old state becomes active friction.

Cache Debt is not only a storage problem. It changes the cost, speed, quality, and explainability of AI work.

  1. 01
    It raises operating cost.

    Redundant files, embeddings, logs, and model caches consume storage and compute without adding current value.

  2. 02
    It weakens output quality.

    Stale context and old prompt versions can steer models toward answers that no longer match the system.

  3. 03
    It hides the cause of failures.

    When retained state has no owner or history, teams spend longer separating current behavior from forgotten residue.

Where it appears

Every retained layer can drift.

Look beyond one cache folder. Debt forms wherever AI systems preserve information without a review cycle.

01

Context layers

Saved instructions and conversation state keep accumulating after their useful life ends.

02

Prompt systems

Old prompt versions remain active, duplicated, or detached from the behavior teams expect.

03

Vector stores

Embeddings drift away from their source material without a refresh or deletion policy.

04

Agent workspaces

Logs, tool results, generated files, and temporary state become permanent by accident.

Reduce the debt

Make retention an explicit decision.

  1. 01
    Inventory

    Find what the system retains, where it lives, and which workflow still depends on it.

  2. 02
    Classify

    Separate active material from stale, duplicated, orphaned, or unknown state.

  3. 03
    Refresh or remove

    Update valuable state and safely delete material that no longer earns its place.

  4. 04
    Set retention rules

    Give every cache, prompt, embedding, and artifact an owner and review interval.

Who should track it

The work crosses team boundaries.

AI engineers, platform teams, developer-experience teams, security teams, and technical leaders each see a different part of the same maintenance problem.

Start with what your system keeps.

Open the checklist