Cache the Meaning, Not the Words: Semantic Caching for AI Calls

This one's speculative — a thought experiment about design principles, not a report on something I've built.

The obvious cache misses almost everything

Standard caching keys on exact input — same string in, same string out, don't recompute. That catches almost nothing in a real AI system, because the same underlying question rarely arrives phrased identically twice. "What's my account balance" and "how much money do I have" are the same request wearing different words, and a string-keyed cache treats them as two unrelated calls to pay for in full.

Keying on embedding similarity instead

The fix is caching on semantic similarity rather than exact text: embed the incoming request, compare it against embeddings of recent cached requests, and serve the cached response when similarity clears a threshold instead of re-running inference. That turns a cache from "only helps on literal repeats" into "helps on the much larger set of requests that mean the same thing."

The threshold is the whole design problem

Set the similarity threshold too loose and you serve a cached answer to a question that was subtly different in a way that mattered — confidently wrong, and silently so, because nothing failed loudly. Set it too tight and the cache barely fires at all, and you've built infrastructure that saves nothing. The right threshold isn't a fixed constant; it should tighten for anything consequential and can loosen considerably for low-stakes, high-volume traffic where an occasional near-miss costs nothing.

Staleness is a second problem hiding behind the first

A semantically cached answer can be similarity-matched correctly and still be wrong, if the underlying facts changed since the response was cached. A balance-related answer cached an hour ago and served now is a different kind of wrong than a paraphrase mismatch — the design needs a separate, deliberate answer to "how fresh does this need to be" for each category of request, not one blanket expiration policy applied everywhere.

I'm Jesse Myers — Marine veteran, 32 years in enterprise IT, now building production AI systems. This site is where I write about what I've actually built, and occasionally about ideas I haven't built yet but think are worth taking seriously.