Your RAG system worked perfectly in October 2025. By November, users report declining accuracy. What happened? Embedding drift—the gradual degradation of vector representations as language evolves, new terminology emerges, and embedding models improve. This guide covers detection, monitoring, and automated remediation strategies from production deployments managing 50M+ vectors.
Embedding drift occurs when:
- **Model updates**: OpenAI updates text-embedding-3-large, changing vector semantics
- **Language evolution**: New terminology emerges ("GPT-5", "agentic AI"), old embeddings don't capture it
- **Domain shift**: Your corpus changes focus, but old embeddings reflect outdated priorities
- **Query distribution change**: Users ask different questions than during initial indexing
- **Data staleness**: Documents are updated but embeddings remain unchanged
- **Monitor continuously**: Track drift metrics daily, alert on 20%+ degradation
- **Log everything**: Store query embeddings, scores, user feedback for analysis
- **Version embeddings**: Tag vectors with model version, enable model comparison
- **Incremental reindex**: Reindex high-traffic documents first, low-traffic later
- **A/B test**: Route 10% traffic to new embeddings before full cutover
- **Keep old index**: Maintain backup for 30 days post-reindexing
- **Document triggers**: When you reindexed and why (for future reference)
- **Automate validation**: Use held-out test set to measure retrieval quality
Embedding drift is inevitable in production RAG systems. The solution isn't preventing drift—it's detecting and remediating it systematically. Monitor retrieval scores, zero-result rates, and user satisfaction. When drift exceeds 30%, trigger automated blue-green reindexing with validation. This maintains RAG quality over time without manual intervention.