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The article warns that when migrating to a new embedding model in production, previously calibrated trust scores and thresholds become invalid, yet the system may still produce plausible but subtly wrong outputs, causing silent degradation.
A critical observation about AI memory systems: trust scores tied to embedding models break when the model is swapped, and recalibration becomes meaningless because the embeddings change. The author questions whether anyone has solved this without rebuilding trust logic.