Vector Databases

Retrieval Latency Spikes in Production RAG: Diagnosing the Real Bottleneck

Your Retrieval-Augmented Generation (RAG) application performs well during testing but suddenly experiences latency spikes in production. The problem isn't always the LLM. Learn how to identify whether your bottleneck is the embedding service, vector database, document retrieval, network, reranking,

Jul 28, 2026 6m read 👁 16

Embedding Drift Is Breaking Your Recommendation Model in Production

Your recommendation model performed exceptionally during offline testing, but click-through rates dropped after deployment. The culprit may not be the ranking model—it could be embedding drift. Learn what embedding drift is, why it happens, how it affects recommendation systems, and practical

Jul 28, 2026 6m read 👁 2

Embedding Similarity Scores That Lie: Why High Cosine Scores Miss Intent

High cosine similarity scores don't always mean semantic relevance. Learn why embedding-based search can miss user intent, how false positives occur, and the techniques used to improve retrieval accuracy in AI systems.

Jun 16, 2026 5m read 👁 31