Embedding Drift
A persistent operational failure mode in long-running vector retrieval systems where stored embeddings progressively diverge from the semantics of incoming queries. Caused by upgrades to the foundational embedding model (e.g., migrating from a 2024 text encoder to a 2026 multimodal encoder), evolving enterprise vocabulary, and accumulating distribution shift in the user-query population. The pipeline keeps running cleanly, but retrieval relevance silently degrades — recall metrics drop without surfacing as an error.
Definition
A persistent operational failure mode in long-running vector retrieval systems where stored embeddings progressively diverge from the semantics of incoming queries. Caused by upgrades to the foundational embedding model (e.g., migrating from a 2024 text encoder to a 2026 multimodal encoder), evolving enterprise vocabulary, and accumulating distribution shift in the user-query population. The pipeline keeps running cleanly, but retrieval relevance silently degrades — recall metrics drop without surfacing as an error.
Connections 4
Outbound 2
Inbound 2
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