Cache Index Lab / 4 articles
Understand the reusable work inside an AI workflow.
AI caching covers several mechanisms with different boundaries. A stable prompt can be processed again under a provider’s reuse rules, a retrieval system can retain source results, and an application can save a final answer. Agent memory adds another purpose: retaining context across steps or sessions. This collection explains those distinctions through practical design and measurement questions.
Use the articles to define the unit being reused, the versions that must match, and the evidence that a result remains suitable. Pay attention to the difference between an observed usage field and a cost estimate. When exploring semantic matches, bring difficult near-miss examples as well as repeated questions; the most revealing cases often look similar while requiring different answers.
A similar question can still require a different answer. Learn which changes invalidate semantic cache entries and how to migrate without silently reusing incompatible results.
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A cache-hit percentage is only useful when its denominator is clear. Learn how to reconcile token reuse, request counts, and task cost.
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A remembered preference, an active task, and a reusable answer need different rules for identity, updates, retention, and evaluation.
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A stable prompt prefix can make repeated context reusable. The benefit depends on useful repetition, careful versioning, and measured behavior.
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Separate the AI work you can reuse from the decisions that need a fresh answer.
AI & agentsGive stable instructions and changing conversation context different jobs.
AI & agentsKeep reusable observations separate from decisions, memory, and actions.