What is semantic caching in RAG?

Semantic caching stores past answers keyed by the meaning of a query rather than its exact text, so a new question that's close enough to an old one gets served the saved answer instead of re-running the whole RAG pipeline. "How do I reset my password?" and "I forgot my password, what now?" are different strings but the same intent — a semantic cache treats them as a hit.

How is it different from exact-match caching?

A normal cache only fires when the input matches character-for-character, so any rewording misses. A semantic cache embeds each incoming query, compares it against the embeddings of past queries, and returns the cached answer when the similarity clears a threshold you set. That means it catches paraphrases, typos, and reordered wording — the huge majority of real user variation that exact-match caching sails right past.

Why bother?

Cost and speed. A cache hit skips embedding the query, searching the vector store, and — the expensive part — calling the LLM to generate. You return a stored answer in milliseconds for a fraction of the price.

For high-traffic apps where users ask the same handful of things a thousand different ways, the savings are large and the latency drop is immediate. It's most valuable in support bots, FAQ assistants, and internal tools, where a small set of common intents accounts for most of the traffic.

What's the catch?

Two things, and they're the whole trade-off. First, staleness: if the underlying answer changed but the cache still holds the old one, users get outdated information until you expire or invalidate the entry. Second, false hits: set the similarity threshold too loose and two questions that only look similar get the same answer — someone asking about the 2024 policy gets the 2023 one.

Tuning that threshold, and setting sensible expiry, is the real work. Too strict and you barely cache anything; too loose and you confidently serve wrong answers.