Guide
How to cache LLM responses in production without losing control
The cleanest production pattern is simple: ask the cache first, call the model only on miss, then save the response back with the same namespace and prompt hash.
Implementation flow
- • Send the full prompt to the cache first
- • Return immediately when the cache hits
- • Call your model provider when the cache misses
- • Save the fresh response back to the cache
- • Track hit rates and namespace behavior over time
What to control
Namespaces let you separate environments, applications, or tenants. TTLs control freshness. Your app should still own model retries, streaming, moderation, and safety logic.
What to cache first
Start with repetitive workflows: support prompts, internal copilots, QA traffic, or RAG queries with recurring intent. These usually produce the fastest visible gains.
Next links
For a category overview, read the LLM cache page. For exact vs semantic behavior, compare prompt caching vs semantic caching next.
Related guides
FAQ
How do you cache LLM responses safely?
Check the cache before the provider call, keep namespaces scoped correctly, apply your own validation or safety filters, and then save successful responses back into the cache with an explicit TTL strategy.
Should I cache every LLM response?
No. Cache responses for workloads that are stable enough to benefit from reuse, and use namespaces or exclusions when model-specific behavior or user-specific data should remain isolated.
What is the basic implementation pattern?
Ask the cache first, call the model on miss, save the fresh answer, and monitor hit rates over time.
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