Discussion on "Semantic Caching for LLM Calls in ASP.NET Core: When to Use It and How"

Coding Droplets · 2026-08-16T12:00:00.000Z

Every AI feature I have shipped eventually hits the same wall. Users ask the same twenty questions in a hundred different phrasings, and each one costs a full round trip to the model. Semantic caching

The cost point hits home. I’ve seen repeated prompts quietly eat through API budgets. Semantic caching helps, but only if you measure the hit rate first.

Hi Julian, Thanks! Agreed. Measuring comes first.

One thing I'd add. Hit rate on its own can flatter you. A loose threshold looks like a great result until you check false hits next to it. I measure duplicate rate in shadow mode before building anything, then track both numbers together.

The threshold tuning is where semantic caching quietly gets dangerous, since a threshold loose enough to catch paraphrases will eventually serve a cached answer to a question that only looks similar but needs a different reply. I keep a small labeled set of near-miss pairs and measure false-hit rate whenever I move the threshold, so a cost win does not turn into a wrong-answer regression. How are you deciding your similarity cutoff, and do you track how often a hit returns a semantically-off answer?

Hi Kartik, Thanks! Threshold tuning is the part most people skip.

No fixed number. The distribution shifts with the embedding model, so I shadow-run the cache for a week, log top scores without serving anything, then label a few hundred real pairs and find where false hits fall off.

On tracking, rephrase rate is the giveaway. If people re-ask more after a hit than a miss, the threshold's too loose.

Thanks again for sharing yours.

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Semantic Caching for LLM Calls in ASP.NET Core: When to Use It and How

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