Implementation guide

How to cache LLM responses in production without losing control

Add one cache check before your LLM call. If PromptCacheAI finds a saved answer, return it immediately. If there is no match, call your model as usual and save the new response for future requests.

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Implementation sequence

Capability

Step

What your app does

1. Choose namespace

Pick a boundary such as support-prod, tenant-a, or docs-rag.

PromptCacheAI keeps matching, TTLs, and entries isolated inside that namespace.

2. Check cache

Send prompt, namespace, provider, and model to /chat.

Return immediately on exact or semantic hit.

3. Call model on miss

Use your existing provider call, streaming, retries, and safety filters.

Stay out of the provider path until your app saves a response.

4. Save response

Save the final validated answer with prompt_hash and namespace.

Future exact or similar prompts can reuse it until TTL expiry.

Start with one workflow

Choose a workflow where repeated intent is obvious: support questions, internal knowledge answers, stable RAG queries, demos, QA, or evaluation prompts.

Do not begin with prompts that need a user-specific answer, such as shipping status, account details, billing, or private records. Start where the same answer can safely be reused for repeated prompts.

Namespace examples

• support-bot-prod for production support traffic
• support-bot-staging for QA and demos
• tenant-acme-docs for tenant-isolated RAG answers
• gpt-4o-prod and claude-prod when model-specific separation matters

Streaming guidance

Check PromptCacheAI before starting a stream. On a hit, return the cached text immediately. On a miss, stream from the provider as usual, collect the completed answer, run your validation or safety checks, then save the final response.

Production safety checklist

• Use caching for repeated prompts with reusable answers
• Use live calls for personalized prompts or live user data
• Use TTLs that match how long the answer should remain useful
• Keep retries and provider errors in your app
• Monitor low-hit namespaces for prompts that need normalization
• Review and edit high-value cached answers in the dashboard

Related guides

Prompt caching API

Use concrete request and response examples.

LLM cache architecture

Understand the broader production cache layer.

LLM cache dashboard

Use metrics and cache entries to verify the integration is working.

What is the basic pattern for caching LLM responses?

Check the cache before the provider call, call the model on miss, save the successful response with the returned prompt hash, and monitor hit rates over time.

Should I cache every LLM response?

No. Cache stable, repeatable workflows first. Use live model or source-system calls for personalized prompts, shipping status, billing, account records, and other user-specific data.

How does caching work with streaming responses?

Check the cache before starting the stream. If the cache misses, stream from your provider normally, collect the final text, then save that completed response after your app validates it.

Try PromptCacheAI in your stack

Launch a provider-agnostic prompt caching layer with namespaces, TTL controls, semantic matching, and usage visibility.

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