Caching is a semantic compromise.

In traditional software, cache hits are binary: inputs match exactly or they do not. In probabilistic runtimes, we cache semantically similar queries by mapping natural language to vector embeddings and testing if they fall within a defined similarity range.

This approximation introduces a fundamental compromise. We treat different words as carrying the identical intent, trading the precise context of a unique user query for operational speed and cost reduction.

If the similarity threshold is calibrated too loosely, the system merges distinct intents, serving incorrect answers to users under the guise of efficiency. True semantic cache design requires treating similarity thresholds as active semantic boundaries.

For a deeper analysis of similarity gate calibration, see Semantic Caching for Probabilistic Systems.

What this changes in practice: Maintain high similarity thresholds to protect intent, and treat any cached response as a semantic approximation rather than a literal duplicate.