PathweysAI Engineer Track · Enterprise Deployment
Prompt & Semantic Caching
Prompt & Semantic Caching
Two different caching layers cut cost on repetitive AI workloads: prompt caching, where a provider reuses the computed state behind a stable, repeated prefix of a request, and semantic caching, an application-level layer that reuses whole responses for requests with similar meaning. Together they meaningfully cut cost and response time, but only when applied carefully.
Why this matters
Real traffic to these systems is often highly repetitive, which makes caching one of the highest-return cost levers available after model selection itself. But caching the wrong thing, a personalized or stateful response, serves the wrong answer to the wrong person, so knowing what is actually safe to cache matters as much as the caching mechanism itself.
Prompt caching
Prompt caching reuses the computed prefix of a prompt across repeated requests, so a system prompt and shared instructions get processed once rather than on every single call. The real leverage comes from ordering: stable content, the system prompt, tool definitions, few-shot examples, has to come first so it forms one genuinely cacheable prefix, with variable content, the actual user turn, placed after it. Reordering an existing prompt so its cacheable portion leads can cut both cost and latency dramatically with no other change required.
Prompt caching is automatic prefix reuse: put stable content first and variable content last, and the stable portion gets paid for once rather than on every single call.
Caching strategy: exact and semantic
Caching operates at several distinct layers that work best combined deliberately rather than relying on just one. An exact cache returns an identical cached response for an identical request. A semantic cache returns a cached response for a request that is merely similar in meaning rather than identical in wording. And prompt caching, handled automatically by the provider, caches the stable prefix of a request regardless of what else layers on top of it. The overall strategy follows directly: exact caching for genuine repeats, semantic caching for paraphrases, and prompt caching for the shared prefix, with each layer trading a small amount of risk, serving a wrong or stale answer, for a meaningful amount of cost and latency savings, which is exactly why time-to-live limits and careful scoping apply to every layer.
Layer exact, semantic, and prompt caching together: exact for genuine repeats, semantic for paraphrases, prompt caching for the shared prefix. Apply a time-to-live to every layer so a stale answer never gets served indefinitely.
Worked example
A customer-support system that receives dozens of differently worded variations of 'how do I reset my password' every day serves nearly all of them from a semantic cache after the very first one is answered, while a system that only implemented exact-match caching would still pay full cost and latency for every single rewording of that same underlying question.
Practice
Add both caching layers to a real workload and measure the resulting cost and latency reduction, along with the critical wrong-cache rate. Produce a documented 'safe to cache' policy, measured savings, and a measured false-hit rate you are explicitly willing to accept.
Interview checks
- Why does prompt caching require a specific ordering of content to actually work?
- What is the practical difference between an exact cache and a semantic cache?
- Why can caching a personalized response be actively harmful rather than merely wasteful?
Common failure modes
- Caching a personalized or stateful response, serving one user's answer to a completely different user.
- No invalidation at all when the underlying data or prompt actually changes, leaving stale answers served indefinitely.
- Confusing a provider's prompt cache with an application-level semantic cache, when the two are genuinely different layers with different mechanics.
- Setting a similarity threshold too loosely, causing the semantic cache to serve wrong answers as though they were exact matches.
Resources
- Anthropic Prompt Caching — Covers the provider-layer cache directly, including the stable-prefix rule and how cache hits are priced.
- GPTCache — The reference application-layer semantic cache; its threshold and eviction design is worth studying directly.
Expert signals
Layering provider-level prompt caching and application-level semantic caching deliberately rather than relying on just one, scoping semantic caching specifically to requests known to be safe with a measured false-hit tolerance, and having real invalidation in place whenever the underlying prompt or data changes, is what turns caching into a genuine cost lever rather than a source of quietly wrong answers.
Confidence
medium