Prompt caching
Weigh the tradeoffs
A longer cached prefix can save more input work but may carry irrelevant context and increase uncached misses when any early token changes. Explicit caches improve control but require lifecycle management.
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The live tension
A longer cached prefix can save more input work but may carry irrelevant context and increase uncached misses when any early token changes. Explicit caches improve control but require lifecycle management.
Translate into user impact on the policy-manual assistant when tuning prompt caching. Which error class costs more—missed catches, slower answers, higher spend, or privacy exposure? That ranking picks the default more honestly than a blog’s recommended settings for prompt caching.
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Numbers that force honesty
without caching: 12.3M input tokens/day; with 90% prefix hits: 1.2M uncached prefix + 0.3M suffix = 1.5M full-price-equivalent tokens before cache-read pricing Scoped specifically to prompt caching / policy-manual assistant / tradeoffs.
If the aggressive prompt caching setting wins the headline metric while breaking a protected slice or blowing the latency budget on the policy-manual assistant, it is not a win. Record intended gain and tolerated regression together for prompt caching.
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Make it operational
Revisit the prompt caching tradeoff when traffic shape changes on the policy-manual assistant. A setting that was right at low volume can fail when a new language segment or document length appears. Tradeoffs expire; re-measure on a calendar, not only on incidents.
Also pin one numeric memory from this prompt caching chapter: without caching: 12.3M input tokens/day; with 90% prefix hits: 1.2M uncached prefix + 0.3M suffix = 1.5M full-price-equivalent tokens before cache-read pricing That number is not decoration; it is a template for how claims about prompt caching on the policy-manual assistant should look in design docs. Scoped specifically to prompt caching / policy-manual assistant / tradeoffs.
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Common mix-ups
People confuse prompt caching with neighboring buzzwords when debugging the policy-manual assistant. Before changing prompts, ask whether the broken stage was evidence gathering, the prompt caching judgment itself, validation, or the product action. Fixing the wrong stage creates folklore (“we tried prompt caching and it failed”) that blocks the next team on the policy-manual assistant. Scoped specifically to prompt caching / policy-manual assistant / tradeoffs.
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Rehearsal (prompt-caching/tradeoffs)
Write a five-line artifact for this page: goal, inputs, check, owner, stop rule. Invent one fluent failure that the check would catch. Keep details specific to prompt caching rather than generic AI advice.
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Rehearsal (prompt-caching/tradeoffs)
Write a five-line artifact for this page: goal, inputs, check, owner, stop rule. Invent one fluent failure that the check would catch. Keep details specific to prompt caching rather than generic AI advice.
Read
Rehearsal (prompt-caching/tradeoffs)
Write a five-line artifact for this page: goal, inputs, check, owner, stop rule. Invent one fluent failure that the check would catch. Keep details specific to prompt caching rather than generic AI advice.
Go deeper
Before you start
Why this matters
For the policy-manual assistant, name one regression you will tolerate when pursuing the main benefit of prompt caching, and one regression that is stop-ship.
In the wild
See how this idea shows up as a product and a company — then come back to the lesson. Skills transfer across vendors.
Related lessons
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Page assessment
Answer from memory. Completion is saved from this evidence, not from opening the next page.
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