When I build a small AI workflow after work, the first constraint is rarely model capability. It is repetition. The same repository rules, Java module conventions, approved commands, fixture definitions, and response contract can be sent on every turn before the workflow does any useful work. The prototype is still impressive. The bill, latency, and maintenance burden are simply hiding in the preamble.
OpenAI added a 12-hour prompt-cache option this month. The detail that matters is not the feature name. A longer cache window makes it possible to reuse a deliberately stable prefix across work performed hours apart, while charging more to write that cache. That is a useful engineering signal: context design is becoming a cost and latency architecture decision, not only a prompting exercise.
Shared context should have an owner
A cache rewards sameness. That is exactly why teams should not respond by placing every policy, document, and recent transcript at the front of every request. A large, frequently changing prefix misses the cache and turns the system prompt into an unreviewable application database. It also makes it hard to tell whether a bad answer came from a model change, stale instructions, or accidental context drift.
I would separate context into three explicit layers. First is the stable contract: role, permitted tools, output schema, security rules, and a small set of durable project conventions. Second is versioned reference material: a repository map, API specification, or approved test patterns, refreshed through a controlled build step. Third is the live task: the target class, failure log, customer question, or approval request. Only the first layer is an obvious caching candidate; the second should earn that status through measured reuse, and the third should remain per request.
Cache hits are not a quality metric
It is tempting to treat a high hit rate as optimization success. It can instead be a warning that a workflow is carrying obsolete policy. For a JTestGen-style Java test workflow, a cached prefix may correctly preserve the Maven command allowlist and test-only write boundary, but it must not freeze an old module layout or a prior test failure as if it were ground truth. The workflow still needs fresh source selection, deterministic compilation, and a visible Maven result.
This is the same tradeoff enterprise teams already understand from build caches and dependency layers. A cache is valuable because it avoids recomputing a known artifact. It becomes dangerous when nobody can answer what produced that artifact, which version is active, or when it should be invalidated. Prompt caching needs the same discipline: a context version, a source revision where relevant, an owner, an expiry policy, and a small regression set that exercises the behavior the context is meant to preserve.
Measure the whole request, not just tokens
The economics are not automatic. A more expensive cache write only pays back when the stable prefix is reused enough, within the configured retention period, to offset it. More importantly, cheaper input does not compensate for a slow tool call, an unnecessary retry, or a reviewer who cannot understand why an agent selected a risky action. Cost per successful, accepted outcome is the better unit than cost per request.
For one workflow, I would log four compact fields alongside ordinary quality evidence: context version, cached versus uncached input, end-to-end latency, and outcome. Then compare a stable baseline against a candidate design on a representative task set. If the change lowers cost but raises repair attempts, slows human review, or weakens groundedness, it has not improved the product. If it lowers repeated setup cost while keeping the same verification and handoff quality, it has created room to invest in the parts users actually notice.
Start with one repeated path
I would not begin with an organization-wide prompt library. Pick one repeated path with a bounded audience: generating unit tests for a known Java service family, triaging a narrow class of CI failures, or summarizing an approved operational report. Write down the immutable prefix, the data that must remain live, and the event that invalidates each reference artifact. Run it long enough to see a realistic mix of fresh and repeated work.
The lesson is modest but durable. Better models will keep reducing the cost of generation. Teams that get more value from them will be the ones that make shared context small, inspectable, and deliberately reusable. A prompt cache is not merely a billing feature. It is an invitation to give the hidden foundation of an AI workflow the same design care as code, data, and deployment.