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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Cache a completed LLM answer only when you can prove that the new request has the same answer-affecting context as the request that produced it. In practice, that means an exact, versioned cache key; explicit tenant and authorization scope; a TTL chosen for the data’s volatility and the cost of being wrong; and invalidation when prompts, models, schemas, source documents, or permissions change.
Keep this separate from provider prompt-prefix caching. Prompt caching reuses model-side processing for a matching prompt prefix while the model still reads new input and generates a response. A response cache returns an answer that was generated earlier, so request equivalence and freshness—not just latency and cost—become application responsibilities.
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The two kinds of caching solve different problems
“Caching the LLM” can describe two independent mechanisms. Confusing them is the quickest way to design the wrong safety controls.
| Mechanism | What is reused | What counts as a hit | What the caller receives | Main correctness risk |
|---|---|---|---|---|
| Provider prompt-prefix cache | Model-side key-value tensors for an eligible, matching prompt prefix | The provider finds a matching prefix under its rules | A newly generated response using the reused processing state | Usually a miss, extra cost, or latency—not an old answer being returned |
| Exact-match response cache | A completed answer and its metadata | The canonical request identity and all required versions match exactly | The previously generated answer, without a new generation call | Returning an answer for changed data, instructions, scope, or user intent |
| Semantic response cache | A completed answer selected for a similar earlier request | An embedding or similarity search passes an application policy | A prior answer judged equivalent enough for the new request | Paraphrases can hide changed dates, entities, negation, constraints, or permissions |
OpenAI describes prompt caching as storing key-value tensors rather than simply storing prompt text; when a prefix matches, processing state is reused and the model still processes new input and generates the response. See OpenAI’s prompt-caching guide. Microsoft likewise states that prompt caching “has no impact on the output content returned in the model response beyond a reduction in latency and cost” in its Azure OpenAI documentation. Neither behavior is a substitute for an application response cache, and neither makes an old completed answer current.
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Start by defining what makes two requests equivalent
Before selecting Redis, a database, an in-process map, or a semantic index, write down every field that can change the answer. Your cache key should contain those fields or a stable fingerprint of them. Include only answer-affecting data, but never omit a field merely to increase the hit rate.
Fields commonly required in a response-cache fingerprint
| Key component | Why it can change the answer | Typical representation |
|---|---|---|
| Normalized user request | Wording, numbers, dates, negation, units, and constraints determine intent | Unicode-normalized text with whitespace canonicalized; preserve meaningful punctuation, casing, dates, and signs |
| Conversation state | A follow-up such as “make it shorter” depends on earlier turns | Stable conversation or turn fingerprint, not an unbounded raw transcript |
| Model and generation settings | Different model versions, temperature, tools, seed, max tokens, or response format can produce different outputs | Model identifier plus a canonical settings object |
| System and developer instructions | Policy, tone, tool rules, and business logic are part of the answer contract | Prompt-template version or content hash |
| Output schema | JSON fields, enum rules, and formatting requirements affect both validity and content | Schema version or hash |
| Retrieved context | Documents, prices, records, and policies can be added, edited, or removed | Corpus version plus source-document IDs or a dependency hash |
| Tenant, user, and authorization scope | A response may contain private or role-specific information | Tenant ID and an authorization-policy or permission-scope version |
| Locale and time context | Language, currency, time zone, and “today” can change the result | Locale, time zone, and an explicit reference date when used |
This complete fingerprint is engineering guidance inferred from documented prefix matching, version tracking, and freshness concerns; providers do not mandate one universal response-key format. Azure’s semantic-cache tutorial specifically recommends recording model and prompt versions for operations, while provider prompt-cache documentation explains why the exact input prefix matters. See Microsoft’s Azure Managed Redis semantic-cache tutorial and OpenAI’s guide.
Canonicalize without changing meaning
- Trim accidental leading and trailing whitespace and normalize Unicode.
- Represent structured values in a deterministic order, such as sorted JSON keys.
- Normalize equivalent formatting, but preserve distinctions such as “not”, a minus sign, a decimal, a date, a model number, or a quoted identifier.
- Include the conversation turn that supplies unresolved references. Do not key “What about its warranty?” as if it were a standalone question.
- Do not lowercase or rewrite text if capitalization, code, case-sensitive identifiers, or proper names can affect retrieval or generation.
- Hash sensitive components after applying the same canonicalization; a hash is an identifier, not permission to expose the underlying data.
Implement an exact-match response cache first
Exact matching is easier to audit than semantic reuse. It gives up hits for harmless paraphrases, but it makes the equivalence rule explicit and keeps a similarity model from silently deciding that two questions are the same.
- Build the answer context. Resolve the user message, relevant conversation turns, model configuration, prompt versions, schema, retrieval dependencies, locale, and authorization scope before looking up the cache.
- Canonicalize each component. Use deterministic serialization and preserve every distinction that can affect the result.
- Compute a versioned key. A conceptual form is
response:v3:{tenant_scope}:{model}:{prompt_version}:{schema_version}:{context_fingerprint}:{request_fingerprint}. The field names are an implementation example, not a provider requirement. - Look up the entry. Store the answer with creation time, expiry time, dependency versions, model and prompt identifiers, and a safety or review status. Treat a missing or malformed metadata record as a miss.
- Recheck authorization at read time. A key partition is helpful, but a permission change, revoked account, or role update must prevent the old answer from being returned.
- Validate the stored response. Check schema, required fields, tool-result references, and any business rule that would make the answer unusable. A cache hit is not automatically a valid answer.
- Return a hit with provenance. Internally record the cache entry ID, creation time, dependency versions, and hit reason. Expose a user-facing “last updated” value only when it is meaningful and safe.
- On a miss, generate and write atomically. Save the answer and its dependency metadata only after validation. Use request coalescing or a short lock to prevent many identical misses from generating the same answer simultaneously.
Do not cache a partial stream as if it were a completed answer. If generation fails, a tool call times out, moderation blocks the output, or validation fails, leave the response-cache entry absent or explicitly marked unusable.
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Use semantic caching only with an equivalence policy
A semantic cache retrieves prior requests that are close in embedding space. Similarity is useful for finding candidates; it is not proof that the candidate answer applies. “What is the sales tax in Berlin?” and “What was the sales tax in Berlin in 2022?” can be near-neighbors while requiring different answers. “Can user A download this report?” and “Can user B download this report?” may be almost identical text but have different authorization results.
Good candidates for semantic reuse
- Stable, low-risk explanations where paraphrases normally preserve the answer.
- Internal help content tied to an unchanged documentation version.
- Repeated formatting or transformation requests whose source content and instructions are identical.
- High-volume questions where an application-specific evaluation has shown that similarity predicts answer equivalence.
Requests that should usually bypass semantic reuse
- Current prices, availability, incidents, schedules, regulations, weather, or any question containing “today,” “latest,” or another freshness cue.
- Personalized recommendations, account details, medical or financial guidance, legal conclusions, or safety-critical actions.
- Permission checks, private records, tenant-specific policies, or answers whose scope depends on the current user.
- Requests with dates, locations, named entities, units, negation, version numbers, or mutually exclusive constraints unless those values are explicitly matched.
- Prompts that invoke tools or retrieval where the underlying state may have changed since the cached answer was produced.
A conservative semantic-hit gate
- Retrieve one or more nearest candidates with the similarity distance or score.
- Reject candidates from a different tenant, user scope, authorization version, model, prompt version, schema, locale, or retrieval corpus.
- Parse and compare hard constraints: dates, locations, entities, quantities, units, negation, requested format, and freshness words.
- Reject a candidate when the new request asks for current information or when a source dependency has changed.
- Apply an application-specific threshold selected from labeled examples. Do not treat a vendor tutorial’s threshold as universally safe; no universal numeric threshold is established by the cited documentation.
- For borderline or high-impact cases, call the normal retrieval and generation path rather than returning the candidate.
- Log the candidate distance, gate decisions, and final outcome so false hits can be investigated.
Microsoft’s semantic-cache tutorial calls out threshold distance and stale-response reports as useful operational signals. That supports tuning and audit, not a claim that one threshold works for every domain. See the tutorial.
Choose TTL from volatility and the cost of being wrong
A TTL limits how long an entry remains eligible, but it cannot prove that an answer is correct. Amazon’s ElastiCache guidance says TTL should match the use case and how often data or model outputs change: shorter TTLs improve freshness while reducing cache hits. Read the AWS semantic-caching best practices for that trade-off.
| Answer class | Freshness policy | Why |
|---|---|---|
| Live or rapidly changing facts | Very short eligibility or no response cache; refresh from the source before answering | An old answer can be materially wrong even when the wording is identical |
| Business data with known update events | Use event-driven invalidation and a backup TTL | Events remove entries promptly; the TTL limits damage if an event is missed |
| Stable documentation | Longer TTL is reasonable when the document and prompt versions are fixed | The answer’s dependencies change infrequently, so hits can be valuable |
| Personalized or permission-scoped output | Short eligibility, strict scope partitioning, and authorization recheck; often bypass semantic caching | Privacy and access errors matter more than hit rate |
| High-impact advice | Prefer regeneration or authoritative retrieval; if cached, require explicit review and provenance rules | The cost of a stale or unsuitable answer outweighs a generation call |
Do not confuse this application TTL with a provider’s prompt-cache retention. They control different objects: your TTL governs whether an old answer can be returned, while provider retention governs whether model-side prefix state may be reused.
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Invalidate when a dependency changes, not only when time passes
Versioning makes stale entries unreachable without scanning the entire cache. Include a prompt-template version, output-schema version, model configuration version, and retrieval-corpus or document version in the key. When one changes, increment the relevant version and old entries stop matching. This is implementation guidance based on documented version tracking and freshness considerations, not a provider-mandated format.
Dependency-aware invalidation
- Prompt or policy edit: increment the prompt or policy version before deploying the new template.
- Schema change: use a new schema version; do not assume an old JSON answer can satisfy new required fields.
- Model or settings change: include the new model identifier and generation configuration in the key.
- Source document edit or deletion: invalidate entries that list that document as a dependency. If document-level tracking is unavailable, bump the corpus version or use a shorter TTL.
- Permission change: advance the authorization-scope version or purge affected tenant and user partitions immediately.
- Emergency correction: maintain an explicit purge path by key prefix, tenant, document ID, or deployment version.
For retrieval-grounded answers, store source IDs and a corpus fingerprint alongside the answer. If a source is removed, an entry that depends on it should become ineligible even if its TTL has not expired. When the system cannot identify dependencies reliably, the safe choices are a conservative TTL or no response caching for that request class.
Keep authorization and privacy outside the similarity decision
A semantic score cannot establish that a caller is entitled to see an answer. Partition entries by tenant and, where necessary, by user or authorization scope. Recheck current authorization immediately before returning a hit, because permissions can change while an entry is still live. Never let a shared “similar question” result bypass row-level, document-level, or tool-level access checks.
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Do not put raw sensitive prompts into metrics merely to debug cache behavior. Log a request fingerprint, scope identifier, versions, decision reason, and timing fields; retain the minimum text needed for an approved audit process. Apply the same retention, deletion, and access controls to cache contents as to generated responses. If a user requests deletion, remove both the answer and searchable representations that could retrieve it.
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Provider prompt caching: what it does and does not change
OpenAI prompt-prefix caching
OpenAI says prompt caching is enabled by default for supported models and reuses matching prompt prefixes. Its guide documents a minimum cacheable prompt length of 1,024 tokens for GPT-5.6 and later; earlier model families have different thresholds and behavior. These are provider-specific details that can change, so verify the live OpenAI documentation before relying on them.
The guide also documents prompt_cache_options.ttl with a 30m value for GPT-5.6 and later. It describes that value as the default and minimum eligibility period after a write or reuse, while the provider may retain entries longer; earlier models have different retention controls. This setting affects reuse of prompt processing state, not whether your application may return an old completed answer.
Azure OpenAI prompt caching
Azure’s documentation emphasizes that prompt caching reduces latency and cost without changing response content. A prompt-cache hit therefore does not mean that Azure selected a previous response for a new user request. Your application still needs response-key versioning, freshness rules, and access checks if it stores completed answers. See Microsoft Learn’s prompt-caching page.
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Amazon Bedrock prompt caching
Amazon Bedrock documents controls for cached prompt prefixes and says that the TTL resets after a successful cache hit. That provider behavior concerns model-side prompt state; it does not invalidate or refresh an application response-cache entry. Consult the Bedrock prompt-caching documentation for model-specific eligibility and retention details.
Instrument correctness, not just efficiency
A high hit rate proves only that your lookup rule is permissive or your workload is repetitive. It does not prove that returned answers are current, authorized, or equivalent. Record enough metadata to explain every decision.
Minimum operational signals
- Hit, miss, bypass, stale, and rejected-hit counts, separated by request class and cache type.
- Lookup latency, backend generation latency, end-to-end latency, and estimated generation or input-token savings.
- For semantic matching, candidate distance or score, selected threshold policy, and hard-constraint rejection reasons.
- Model, prompt, schema, corpus, and authorization versions for each read and write.
- Cache read, write, serialization, eviction, timeout, and connection errors.
- Stale-response reports, user corrections, safety escalations, and sampled human-review results.
- Cross-tenant or authorization-block events; these should be treated as security incidents, not ordinary misses.
Azure’s semantic-cache material lists model and prompt version, latency, cache errors, threshold distance, and stale-response reporting as useful signals. Use those categories as a starting point, then add domain-specific correctness measures. Keep efficiency dashboards separate from quality dashboards so a rising hit rate cannot hide a rising correction rate.
Test the cache with adversarial pairs
- Change one date, number, unit, location, or named entity while keeping all other words the same.
- Add or remove a negation: “does support” versus “does not support.”
- Change the tenant, user role, document permission, or authorization version.
- Change one source document while leaving the question unchanged.
- Change only the system prompt, output schema, model, temperature, or tool availability.
- Use a follow-up question with a different conversation history.
- Ask for “latest” information after the cached answer’s source has changed.
- Submit harmless paraphrases to measure semantic-cache recall without weakening the rejection rules above.
For each pair, assert whether a hit is allowed, whether the answer is identical, and whether the expected invalidation or bypass event occurred. Include these tests in deployment checks whenever a key component or retrieval pipeline changes.
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The following pseudocode shows the order of checks. It is deliberately provider-neutral; adapt storage, embedding, authorization, and generation calls to your stack.
context = resolve_context(request, conversation, auth, retrieval)
key = fingerprint({
request: canonicalize(request),
conversation: context.conversation_version,
model: context.model_and_settings,
prompt: context.prompt_version,
schema: context.schema_version,
sources: context.document_ids_and_corpus_version,
scope: context.tenant_and_auth_version,
locale: context.locale_and_timezone
})
entry = exact_cache.get(key)
if entry and entry.is_eligible(now) and authorize(current_user, entry.scope):
if validate(entry.answer, context.schema):
observe('exact_hit', entry.metadata)
return entry.answer
if semantic_allowed(request, context):
candidates = semantic_index.nearest(canonicalize(request))
for candidate in candidates:
if candidate_matches_hard_constraints(candidate, context)
and candidate.distance < policy.threshold_for(context)
and authorize(current_user, candidate.scope)
and candidate.dependencies_are_current(context):
observe('semantic_hit', candidate.metadata)
return candidate.answer
answer = generate_with_retrieval(context)
validated = validate(answer, context.schema)
if validated:
exact_cache.put(key, answer, metadata=context.cache_metadata,
ttl=policy.ttl_for(context))
return answerThe ordering matters: exact identity and authorization come before semantic similarity; dependency and freshness checks come before returning a candidate; validation occurs both on reads and writes. If any check is uncertain, generate a fresh answer.
Common failure modes and fixes
| Symptom | Likely cause | Fix |
|---|---|---|
| Users see an old policy after a prompt edit | Prompt text was not versioned in the key | Increment the prompt or policy version before rollout and make the old namespace unreachable |
| A tenant receives another tenant’s answer | Tenant or authorization scope was omitted from the key or semantic partition | Partition by scope, recheck authorization at read time, and audit the incident |
| Paraphrases return answers for the wrong date | Embedding similarity ignored a date or freshness constraint | Extract and compare dates; bypass semantic reuse for current or date-sensitive requests |
| Schema validation fails only on cache hits | Old entries were written under a previous output schema | Version the schema and validate every stored answer before returning it |
| Hit rate is high but corrections are increasing | TTL is too long, threshold is too permissive, or dependencies are not tracked | Review stale reports, tighten gates, shorten eligibility, and add source-version invalidation |
| Prompt-cache metrics look good but response quality is unchanged | Prompt-prefix caching was mistaken for response reuse | Measure provider prompt-cache reads separately from application response-cache hits and quality |
| Many identical requests generate simultaneously | Concurrent misses have no coalescing or lock | Use a short single-flight lock, then write one validated result for the waiting callers |
| Entries never disappear after an emergency correction | The system relies only on passive TTL expiry | Add explicit purge by version, dependency, tenant, and key prefix |
Choosing an architecture
| Option | Use when | Advantages | Trade-offs |
|---|---|---|---|
| Exact-match response cache | Correctness and auditability matter more than paraphrase recall | Simple equivalence rule, deterministic invalidation, straightforward testing | Fewer hits when users phrase the same request differently |
| Semantic response cache | Stable, low-risk questions repeat with varied wording and you have evaluation data | Higher potential reuse across paraphrases | Embedding index, thresholds, hard-constraint checks, and false-hit monitoring are required |
| Provider prompt-prefix cache | Requests share long, stable instructions or retrieved prefixes | Can reduce prefill work, latency, and input-token cost without returning an old answer | Provider-specific eligibility and retention; it does not solve response freshness |
| Hybrid | You need both lower generation cost and safe reuse of completed answers | Prompt caching optimizes misses while exact or carefully gated semantic caching handles repeats | Two sets of metrics, retention rules, and failure modes to operate |
For most applications, begin with an exact-match cache for a narrow, low-risk request class. Add semantic matching only after you can demonstrate answer equivalence on representative and adversarial examples. Keep provider prompt caching enabled where supported, but measure it as a separate optimization.
Quick Recap
Production checklist
- Have you listed every answer-affecting input and represented it in the key or dependency metadata?
- Are model, prompt, schema, retrieval, corpus, locale, and authorization versions explicit?
- Can a document update, permission change, or emergency correction make affected entries unreachable immediately?
- Does every read recheck authorization and validate the stored response?
- Are current, personalized, sensitive, and high-impact requests bypassing unsafe semantic reuse?
- Is the TTL tied to volatility and the cost of an incorrect answer rather than a generic default?
- Do metrics include stale reports, corrections, safety escalations, threshold distance, and cache errors—not only hit rate?
- Can operators inspect and purge entries by tenant, dependency, version, and key prefix?
- Have adversarial tests covered dates, negation, entities, permissions, source changes, prompt changes, and schema changes?
- Are provider prompt-cache metrics clearly separated from application response-cache metrics?
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