Mastery: connect the pieces

A complete explanation of Prompt caching has four parts. First: Prompt caching reuses computation for an identical stable prefix, such as a long system prompt or shared document set. It is not the same as caching the final answer: the model can still generate a new continuation from the uncached suffix. Second, the mechanism: Transformer inference computes attention keys and values for each prefix token. A provider can retain or recognize that prefix state and skip recomputing it on later requests whose bytes, ordering, model, and cache rules match. The changing user message belongs after the stable prefix. Cache lifetime, minimum prefix length, and discount rules are provider-specific. Third, the operational controls: prefix ordering; exact serialization; cache breakpoints; time-to-live; model/version pinning; tenant scope; minimum cacheable tokens; stable-versus-dynamic boundary; and metrics for hit tokens rather than request hits. Fourth, the evidence: Measure cache-read tokens, cache-write tokens, hit ratio by token, time to first token, total latency, input cost per request, reuse count per prefix hash, and quality before and after prefix reordering.

Use the scenario as an oral exam: A 12,000-token policy manual is shared by 1,000 daily requests; each request adds 300 unique tokens. Moving the manual before the user message makes the 12,000-token prefix reusable while the 300-token suffix stays dynamic. Defend one design choice, then argue against it using this tradeoff: 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. Savings depend on repetition, provider pricing, and whether latency is dominated by input processing or output generation. Finally, identify which of these failures your design catches and which remain: putting timestamps before the cache boundary, changing whitespace or tool order, cross-tenant cache leakage, assuming a hit without reading provider usage fields, caching sensitive material too broadly, stale instructions, and optimizing a low-volume prompt with no meaningful reuse.

Mastery is not recalling every term. It is predicting consequences before running the system, noticing when evidence contradicts the prediction, and revising the design without moving the goalposts. Keep a decision record containing the workload, baseline, configuration, test set version, results, known limitations, owner, and rollback condition.

A 12,000-token policy manual is shared by 1,000 daily requests; each request adds 300 unique tokens. Moving the manual before the user message makes the 12,000-token prefix reusable while the 300-token suffix stays dynamic.

The worked number is 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. State the unit and denominator whenever you report it. A percentage without a denominator can conceal a tiny sample; a latency without a percentile can conceal slow users; a similarity score without a labeled task can conceal irrelevant neighbors. Compare the observed value with a threshold chosen before seeing the final test result.

Now test the tempting shortcut. Suppose the team optimizes only the most visible metric. The result may look better while the system becomes less trustworthy. The reason is concrete: 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. Savings depend on repetition, provider pricing, and whether latency is dominated by input processing or output generation. This is why the decision record must include both the intended gain and the tolerated regression. If the tolerated regression is unknown, the change is not ready for a consequential workflow.

• Prefer a measured baseline over a persuasive demo.
• Keep versions, inputs, and thresholds reproducible.
• Separate syntactic success from semantic correctness and authorization.
• Escalate or abstain when evidence falls outside the contract.
• Re-evaluate when data, traffic, models, providers, or user goals change.

These rules turn the topic into an engineering decision rather than a slogan. They also make disagreement productive: another person can challenge the assumptions, rerun the evaluation, and reach a documented conclusion.

Give a three-minute teach-back with no slides. Minute one: define the technique and its boundary. Minute two: trace the mechanism using the worked case and calculation. Minute three: defend the chosen controls with evaluation evidence, then name the strongest unresolved failure. Ask the listener to change one assumption and update your recommendation aloud. You have mastered the topic when the recommendation changes for a technical reason—not because the vocabulary changed—and when you can specify the next experiment that would reduce the most consequential uncertainty.