How to Measure Semantic Cache Correctness
Cute_Pause_449 · reddit · 2026-07-12
The author shares a production troubleshooting case for semantic caching: while cache hit rates and latency looked great, the system was actually returning a ton of "semantically incorrect" results because different requests shared too much boilerplate text.
The article pinpoints the issue in metrics design: the system only checked "if it was a hit" without verifying "if the hit was actually applicable." The solution isn't simply raising the similarity threshold, but implementing a tiered approach:
- First, strip or isolate boilerplate text from the embeddings.
- Add an application-level validation layer that checks reusability based on conditions like tenant, task type, model family, language locale, and tool availability.
- Use spot-checking to distinguish between "similar hits" and "correct hits."
The author concludes by asking how teams typically measure semantic cache correctness in production, and whether they use sampled shadow requests or task-specific validators.
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