Repeated stable context—such as a large policy manual, a codebase guide, or a detailed instruction block—can dominate cost and latency. Prompt caching can make repeated requests more efficient when used with the documented cache-control mechanism and compatible request shapes. It is an optimization, not a substitute for a sound context strategy.
Every token of context is an instruction and privacy boundary problem. Select only relevant sources, label their authority, remove expired material, and maintain a version identifier for the policy or knowledge snapshot used in a response.
| Context | Typical lifetime | How to handle it |
|---|---|---|
| Support policy and examples | Days or weeks | Version it, place it early, and cache it when repeated. |
| Tool definitions | Stable across a workflow | Keep definitions narrow and stable; cache them when appropriate. |
| Current ticket and account state | One request | Fetch fresh, minimise it, and do not treat it as reusable policy. |
Stable context changes rarely across many requests: writing standards, approved product policy, a repository guide, or a schema description. Per-request context includes the user question, session-specific facts, and freshly retrieved documents. Caching the latter can create privacy, correctness, and operational problems.
Select context by task relevance rather than pasting every available source into the request.
Prompt caching has documented TTLs and request-shape conditions. A cache hit is not guaranteed simply because two requests look similar. Treat cache behavior as observable infrastructure: log cache-related usage fields when available and benchmark with representative traffic.
Changing a schema or tool set can alter cache behavior. Version these artifacts and test first-request latency separately from steady-state latency.
A source bundle should carry a source ID, version, effective date, owner, and access policy. When a customer asks “why?”, your system should know which documents shaped the answer rather than only retaining generated text.
When policy changes, invalidation must be deliberate. Serving an old cached instruction may be cheaper but wrong.
Measure token input, cache read/write behavior, output tokens, response time, and quality metrics together. Lower token cost is not a win if the shorter context causes incorrect answers or more human escalations.
Minimize sensitive context before caching. Follow the provider documentation for data retention and eligibility; do not infer retention guarantees from a generic cache concept.
When a support manager asks why an answer was given, you should be able to name the policy version and source bundle that shaped it. A plain string pasted into a prompt is difficult to audit or invalidate.
Create a small metadata object for the stable policy and keep request-specific facts beside it instead of mixing everything into a reusable cache candidate.
A simple metadata record makes it possible to invalidate a policy bundle when its underlying content changes.
context_bundle = {
"source_id": "refund-policy",
"version": "2026-07-01",
"owner": "legal-operations",
"access": "support_agents",
}
def is_current(bundle: dict, expected_version: str) -> bool:
return bundle["version"] == expected_version
print(is_current(context_bundle, "2026-07-01"))TrueSplit stable policy from transient customer data before building the request.
stable_policy = "Refunds require an eligible order and review."
request_context = {"order_id": "o-100", "customer_name": "Asha"}
print(stable_policy)
print(request_context["order_id"])Refunds require an eligible order and review.
o-100A fingerprint changes when the policy version changes, which makes cache invalidation visible in code.
import hashlib
def context_fingerprint(source_id: str, version: str) -> str:
return hashlib.sha256(f"{source_id}:{version}".encode()).hexdigest()[:8]
print(context_fingerprint("refund-policy", "2026-07-01"))This split makes it harder to accidentally reuse a customer-specific value.
stable = {"policy_version": "2026-07-01"}
request = {"order_id": "o-100", "customer_name": "Asha"}
print("customer_name" in stable)
print("customer_name" in request)False
TrueA context-mapping lab
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No. It improves reuse economics; source selection and invalidation still determine correctness.
Cold versus warm latency, input/cache usage, output usage, answer quality, and escalation rate.
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