Semantic caching keys on meaning, not the exact string — embed the query, cosine-match the cache, HIT above a threshold
Article summary
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A plain cache keys on the exact string, so "How do I reset my password?" and "I forgot my password, how can I change it?" look like two different requests and both hit the model — even though the answer is identical. Semantic caching keys on meaning : it embeds the incoming query into a vector, measures cosine similarity against previously answered queries, and if the closest clears a threshold it returns that cached answer — a HIT — instead of calling the model, cutting both cost and latency.…
1Key Takeaways
- A plain cache keys on the exact string, so "How do I reset my password?" and "I forgot my password, how can I change it?" look like two different requests and both hit the model — even though the answer is identical.
- Semantic caching keys on meaning : it embeds the incoming query into a vector, measures cosine similarity against previously answered queries, and if the closest clears a threshold it returns that cached answer — a HIT — instead of calling the model, cutting both cost and latency.….
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that a plain cache keys on the exact string, so "How do I reset my password?" and "I forgot my password, how can I change it?" look like two different requests and both hit the model — even though the answer is identical.
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