Build the mental model

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.

A reliable beginner model has three boxes: input, transformation, and evidence. The input is what enters the system; the transformation is what the technique actually computes or changes; the evidence is how we learn whether the output works beyond one attractive example. For this topic, the transformation is not magic: 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.

The boundary matters. Do not confuse a mechanism with an outcome. A mechanism can make a desired outcome more likely while still failing on a particular case. It also does not erase the need for source checks, permissions, or domain judgment. The practical question is therefore not “Does it work?” but “Under which inputs, constraints, and measurements does it work well enough?”

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.

Create one near-example and one counterexample. The near-example should differ from the scenario in only one important way; the counterexample should look similar while requiring a different technique. For each, label the input, the transformation that actually occurs, and the evidence you would accept. This exercise prevents the topic name from becoming an all-purpose explanation. If you cannot say what would falsify your mental model, it is still a story rather than a model. End with one sentence beginning “This technique does not guarantee…” and make that limitation observable.