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Application-level response caching can make an LLM feature faster and cheaper, but a cache hit skips a new generation. The safe design is therefore to reuse an answer only when the new request has the same answer-affecting identity, data, instructions, model configuration, and authorization scope as the original. Keep this separate from provider prompt-prefix caching: prompt caching reuses model-side processing for a matching prompt prefix, while response caching returns a completed answer from your own store. OpenAI describes prompt caching as reusable key-value tensors for matching prefixes; the model still processes new input and generates a response (OpenAI prompt-caching guide).
Prompt caching and response caching solve different problems
The word cache hides two different mechanisms:
| Mechanism | What is reused | What counts as a hit | Main risk | Typical benefit |
|---|---|---|---|---|
| Provider prompt-prefix caching | Model-side key-value state for an eligible, matching prompt prefix | The provider detects a matching prefix according to its model-specific rules | Usually no answer-correctness risk from reuse; a miss simply performs normal processing | Lower prefill latency and input-token cost |
| Exact-match response cache | A completed answer and its metadata | Your canonical request fingerprint matches | Returning an answer for a request whose intent, data, instructions, model, or permissions differ | Skips retrieval and generation entirely |
| Semantic response cache | A completed answer selected for a sufficiently similar earlier request | A similarity search returns a candidate within an application policy | Paraphrases can conceal changed dates, entities, negation, constraints, or access rights | More hits than exact matching, with substantially more validation work |
Microsoft states that prompt caching does not change response content beyond reducing latency and cost (Microsoft Learn). That statement does not describe an application response cache. When your application returns a stored completion, it has deliberately decided not to ask the model again, so freshness and request equivalence become your responsibility.
Start with a cacheability decision
Do not put every LLM call into one cache. Classify requests before generating a key:
- Good candidates: stable, low-risk explanations; repeated transformations with identical inputs; documentation answers tied to an immutable corpus version; expensive reports whose source data changes on a known schedule.
- Conditional candidates: retrieval-grounded answers, account-specific summaries, or answers that depend on a tenant policy. These need source-version and authorization controls.
- Usually bypass: live prices, current incidents, medical or legal decisions, safety-critical actions, requests that explicitly ask for the latest information, and outputs containing secrets or highly personal data.
This classification is an implementation policy, not a guarantee supplied by a cache vendor. If the cost of a wrong answer is high, a cache miss and fresh retrieval are cheaper than an incorrect hit.
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Define what “the same request” means
An exact-match cache is only as correct as its identity function. Write down every field that can change the answer, then include those fields—or a stable version of them—in the fingerprint. The following fields are a practical starting point derived from prompt-prefix matching, version tracking, and freshness guidance; they are not a provider-mandated format.
| Fingerprint component | What to record | Why it matters |
|---|---|---|
| Normalized user request | Whitespace-normalized text, normalized case only where semantics permit, and canonical representations of dates, units, and locale | Prevents harmless formatting differences from destroying hits without erasing meaningful distinctions |
| Conversation state | Relevant prior turns, tool results, conversation revision, or a hash of the retained context | “What is its price?” has a different answer after each preceding turn |
| Model identity | Provider, model name, and model revision or deployment identifier | Different models and revisions can answer the same prompt differently |
| Generation settings | Temperature, top-p, max output tokens, response format, tool mode, seed where supported, and other answer-affecting controls | Changing controls can change the completion or its structure |
| Instruction version | System and developer prompt version, policy bundle, and feature-flag revision | An old answer may violate newly deployed instructions |
| Output schema | Schema name and revision, including required fields and enum definitions | A previously valid JSON object may no longer satisfy the current contract |
| Retrieved context | Corpus revision, document IDs and revisions, embedding/index revision, or a content digest | Answers grounded in changed or deleted documents become stale |
| Scope and authorization | Tenant, user, role, data-region, and policy scope when they affect visible information | Prevents one principal from receiving another principal’s answer |
Include only answer-affecting data, but do not omit a field merely to increase the hit rate. If a field cannot be represented reliably, treat that request class as uncacheable or use a shorter lifetime and a fresh authorization check.
Canonicalize without changing meaning
Canonicalization should remove accidental differences, not semantic ones. Trim and collapse ordinary whitespace, normalize line endings, and serialize structured inputs with sorted keys. Preserve punctuation when it changes meaning, retain minus signs and decimal precision, and normalize dates with an explicit time zone. Do not lowercase an identifier or product code unless that identifier is defined as case-insensitive. For conversation history, hash the exact ordered messages after applying your documented normalization rules.
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A useful key is a namespaced digest rather than raw prompt text. For example:
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cache_key = 'llm-response:v3:' + sha256(canonical_json({
'request': normalized_request,
'conversation_revision': conversation_revision,
'model': model_id,
'generation': generation_settings,
'instructions': instruction_version,
'schema': schema_version,
'retrieval': retrieval_fingerprint,
'scope': authorization_scope
}))The v3 namespace is your own key-format version. Changing the serialization or interpretation rules should change that namespace so old entries cannot be mistaken for new ones.
Build an exact-match response cache step by step
- Decide whether the request class may be cached. Bypass requests that require live data, contain secrets you do not want persisted, or have consequences that demand a fresh decision.
- Resolve identity and scope before lookup. Authenticate the caller, determine tenant and role, and derive the scope that is allowed to see the answer. Never use an anonymous shared key for a permissioned result.
- Gather all answer-affecting inputs. Freeze the model deployment, generation settings, instruction revision, output schema, conversation revision, and retrieval dependencies for this request.
- Canonicalize and fingerprint. Serialize the selected fields deterministically and hash the result. Keep the metadata needed for diagnosis beside the value.
- Look up the entry and validate it. Check expiration, namespace, model and prompt versions, scope, schema, and any dependency status. A key match is not a reason to skip these checks if authorization or revocation can change independently.
- Return a hit with provenance. Record that the answer came from cache, its creation time, dependency versions, and the policy decision. Do not silently present a cached answer as if it were generated moments ago.
- Generate on a miss. Run retrieval and generation normally. If the answer includes tool calls or external side effects, do not replay those effects from a response cache; cache only an explicitly safe, idempotent representation.
- Write atomically. Store the answer, normalized metadata, creation time, expiry, dependency fingerprints, and a cache-format version. Use a bounded size and encryption or access controls appropriate to the data.
- Recheck before delivery when needed. For sensitive answers, verify that the caller’s authorization and the relevant document revisions have not changed during generation.
Use a single-flight or request-coalescing mechanism for concurrent misses so that a burst of identical requests does not produce many generations. This improves cost and load without weakening the identity rules.
Version dependencies so changed answers become unreachable
Time-based expiration is useful, but it is not a correctness guarantee. Put versions or content fingerprints for prompts, schemas, model configurations, and retrieved sources into the key. When one changes, increment its version or invalidate entries that depend on it. Old values can remain physically stored until garbage collection; they are safe if no current key can reach them.
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Use explicit invalidation for events that matter immediately: a revoked permission, a deleted document, a corrected policy, a prompt rollback, or a model deployment change. An invalidation message can delete keys, advance a namespace version, or mark a dependency as unusable. Namespace versioning is often safer than trying to find every physical key.
Set TTL from volatility and the cost of being wrong
A TTL answers “how long may this value be reused if nothing else changes?” It does not answer whether the value is correct right now. Amazon ElastiCache guidance says TTL should match the use case and how often data or model outputs change; shorter TTLs improve freshness but reduce hit rates (AWS semantic-caching best practices).
| Request characteristics | TTL approach | Additional control |
|---|---|---|
| Stable explanation with low consequence | Longer TTL can be reasonable | Still rotate on prompt, model, schema, or corpus changes |
| Frequently changing documentation or operational status | Short TTL | Invalidate on source publication or incident updates |
| Personalized or permissioned data | Very short TTL or no response cache | Partition by scope and recheck authorization on every hit |
| High-impact or safety-sensitive decision | Bypass response caching | Perform fresh retrieval, policy checks, and model evaluation |
Do not confuse this application TTL with a provider’s prompt-cache retention. OpenAI’s current guide says GPT-5.6 and later support prompt_cache_options.ttl with a 30m value described as the default and minimum eligibility period after a write or reuse; the provider may retain an entry longer, and earlier model families have different controls (OpenAI prompt-caching guide). That setting concerns reusable prompt processing, not whether your application returns a completed answer. Amazon Bedrock likewise documents prompt-prefix controls and says the TTL resets after a successful cache hit (Amazon Bedrock prompt-caching documentation).
Use semantic caching only with an answer-equivalence policy
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Filter candidates before comparing similarity
- Partition by tenant, user, role, region, and other authorization scope.
- Require compatible model, instruction, schema, and corpus versions.
- Reject candidates whose extracted dates, locations, named entities, units, or product identifiers differ.
- Reject a candidate when either request contains negation, an explicit freshness term such as “today,” or a different constraint.
- Require compatible conversation state; do not match an isolated question to an answer that depended on unseen prior turns.
- Prefer exact matches for structured identifiers and use semantic matching only for the natural-language portions that truly admit paraphrase.
Make the threshold conservative
Choose a distance or similarity threshold from evaluation on your own traffic. There is no universal safe number in the available provider documentation. Microsoft’s semantic-cache tutorial identifies threshold distance and stale-response reports as useful operational signals, supporting measurement and tuning rather than a fixed threshold (Azure Managed Redis semantic-cache tutorial). For uncertain, changing, personalized, or high-impact requests, call the retrieval and generation path instead of returning the candidate.
Store why a semantic hit was accepted
Save the candidate distance, the filters that passed, the scope partition, versions, and the policy decision. If a user reports that an answer is stale, you should be able to identify the matched entry and the rule that allowed it. Keep sensitive prompt text out of routine logs; use opaque request IDs and redacted features where possible.
Protect authorization and sensitive data
A response cache can become a data-leak mechanism even when the model is perfectly behaved. The cache lookup must occur within an authorization boundary:
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- Use tenant and role partitions in the key for shared infrastructure.
- For user-specific answers, include a stable user or account scope, or exclude the response from shared reuse.
- Recheck current authorization before serving a hit when permissions can be revoked during the entry’s lifetime.
- Do not infer permission from the natural-language prompt; use the authenticated principal and policy engine.
- Encrypt stored values, restrict operator access, define retention for prompts and answers, and redact secrets from metrics.
- When a document is deleted or access is withdrawn, invalidate dependent entries immediately rather than waiting for TTL.
These controls are prudent architecture guidance. Provider prompt-cache documentation does not define a universal response-cache access-control recipe, so your application remains responsible for isolation.
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Instrument efficiency and correctness separately
A high hit rate can mean that an unsafe cache is serving the same wrong answer repeatedly. Track quality signals beside cost and latency:
| Signal | What it tells you |
|---|---|
| Hit, miss, bypass, and negative-cache rates | Whether policy and identity rules are producing the expected traffic mix |
| Lookup latency and backend generation latency | Whether the cache actually improves end-to-end response time |
| Similarity distance or threshold margin | How close semantic hits are to the acceptance boundary |
| Model, instruction, schema, corpus, and scope versions | Which dependency combination produced each answer |
| Stale-response reports and correction rate | Evidence that users or downstream checks found an answer outdated or wrong |
| Safety escalations and policy violations | Whether cached delivery bypassed a safety or authorization control |
| Cache read, write, serialization, and eviction errors | Operational failures that can silently turn into misses or corrupt entries |
| Cost and token savings | Financial benefit, which should never be treated as a correctness metric |
Microsoft’s semantic-cache guidance specifically calls out threshold distance, stale-response reports, model and prompt versions, latency, and cache errors as useful telemetry (Microsoft Learn). Sample returned answers for human or automated review, with privacy controls, and compare them against freshly generated answers for a representative evaluation set.
Provider-specific prompt-cache details
The following facts describe provider prompt-prefix features, not application response caching. They are current details that can change and should be checked against the live documentation before deployment.
| Provider documentation | Documented behavior | What it means for your response cache |
|---|---|---|
| OpenAI | Prompt caching is enabled by default for supported models and reuses matching prompt prefixes. The guide states a minimum cacheable prompt length of 1,024 tokens for GPT-5.6 and later; thresholds and behavior differ for earlier models. | A matching prefix reduces repeated processing; it does not authorize your application to return an earlier completed answer. |
| OpenAI TTL option | For GPT-5.6 and later, prompt_cache_options.ttl supports 30m, described as the default and minimum eligibility period after a write or reuse; retention may last longer. |
Do not copy this provider retention value into your application response TTL. |
| Microsoft Azure | Microsoft says prompt caching has no impact on output content beyond reducing latency and cost. | Prompt-cache hits are not completed-answer hits; your application still needs response identity and freshness rules. |
| Amazon Bedrock | Bedrock documents prompt-prefix cache controls and says TTL resets with a successful cache hit. | Provider retention behavior is separate from your store’s invalidation, authorization, and stale-answer policy. |
A reference request flow
The following pseudocode shows where correctness checks belong. It is intentionally provider-neutral:
def answer(request, principal):
policy = classify_cacheability(request)
scope = authorize_and_derive_scope(principal, request)
deps = resolve_dependencies(request) # corpus, documents, prompt, schema
if policy.bypass:
return generate_fresh(request, principal, deps)
identity = canonical_json({
'request': normalize(request.text),
'conversation': request.conversation_revision,
'model': CONFIG.model_revision,
'generation': CONFIG.generation_settings,
'instructions': CONFIG.instruction_version,
'schema': CONFIG.schema_version,
'retrieval': deps.fingerprint,
'scope': scope.key,
})
key = 'llm-response:v3:' + sha256(identity)
entry = cache.get(key)
if entry and entry.expires_at > now() and valid_scope(entry, scope)
and valid_dependencies(entry, deps):
metrics.hit(entry)
return deliver(entry.answer, provenance=entry.metadata)
result = generate_fresh(request, principal, deps)
cache.put(key, value=result.answer, metadata={
'scope': scope.key,
'model': CONFIG.model_revision,
'instructions': CONFIG.instruction_version,
'schema': CONFIG.schema_version,
'retrieval': deps.fingerprint,
'created_at': now(),
'expires_at': choose_ttl(policy, deps),
})
metrics.miss()
return deliver(result.answer, provenance='fresh')In production, make writes atomic, cap entry size, handle serialization failures as misses, and prevent a failed generation from overwriting a valid newer value. If a response contains a tool action, store only a safe result representation and execute the action through a separate idempotency and authorization mechanism.
Common failure modes and fixes
| Symptom | Likely cause | Fix |
|---|---|---|
| A newly changed instruction still returns old wording | Prompt version is absent from the key | Version the system/developer prompt and advance the namespace on deployment |
| An answer cites a document that was replaced | TTL was used without source dependency tracking | Include document or corpus revisions and invalidate on publication |
| One tenant sees another tenant’s result | Shared key omitted authorization scope | Partition by tenant and role; recheck authorization before delivery |
| Semantic hits answer the wrong year or entity | Vector similarity blurred a date or named constraint | Extract and compare structured constraints; bypass on mismatch |
| Hit rate is high but users report stale answers | Optimization focused on reuse, not freshness | Shorten TTL for volatile classes, add invalidation events, and monitor stale reports |
| Changing temperature has no visible effect | Generation settings were not part of identity | Include every answer-affecting setting or use a fixed configuration |
| Cache hits still incur model cost | The implementation uses provider prompt caching but still generates a response | Decide whether you need prompt-prefix optimization or application response reuse; they are different paths |
| Old entries consume storage after a deployment | Versioned keys made entries unreachable but garbage collection is delayed | Keep namespace versioning for safety and run retention-based cleanup separately |
Deployment checklist
- Document which request classes are cacheable and which always bypass.
- List every answer-affecting field and encode it in a deterministic fingerprint.
- Version model deployments, prompts, schemas, retrieval indexes, and corpus releases.
- Partition entries by tenant and authorization scope; never trust prompt text as permission.
- Choose TTL from data volatility and the cost of an incorrect answer.
- Add event-driven invalidation for document, policy, permission, prompt, and model changes.
- Use semantic matching only with structured constraint checks and an evaluated threshold.
- Log hit decisions, distances, versions, scope IDs, latency, errors, stale reports, and safety escalations without leaking sensitive text.
- Test paraphrases, date changes, negation, unit changes, tenant switches, revoked access, prompt rollouts, schema changes, and deleted documents.
- Review sampled cached answers against fresh answers and track correction rates—not just hit rate and cost.
- Recheck provider documentation when changing prompt-cache settings because retention, token thresholds, and model support are volatile.
How to choose the simplest safe design
Start with an exact-match cache and a conservative bypass policy. It is easier to explain, test, invalidate, and audit than semantic matching. Add semantic retrieval only after you have an evaluation set showing that paraphrases preserve the answer and after you can enforce scope, version, date, entity, and constraint filters. Treat provider prompt-prefix caching as a complementary optimization when repeated prefixes are present; it can reduce generation overhead without changing output content, but it does not remove the need for application-level freshness and authorization decisions.
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