Vector Search

Semantic Cache Misses: Why Identical Questions Bypass Your LLM Cache

Semantic caching can dramatically reduce LLM costs and response times, but many teams discover that seemingly identical questions still bypass the cache and trigger expensive model calls. Learn why semantic cache misses occur, how embedding similarity works, and how to build production-ready caching

Jul 14, 2026 5m read 👁 3

Embedding Quantization Trade-offs: When Shrinking Vectors Kills Recall

Quantization can dramatically reduce vector storage costs and improve search speed, but aggressive compression often comes at a hidden price: lower recall. Learn how embedding quantization works, where performance gains come from, and when shrinking vectors starts hurting retrieval quality.

Jun 23, 2026 5m read 👁 18

Hybrid Search Pitfalls: Why Combining BM25 and Vectors Can Hurt Recall

Hybrid search promises the best of keyword and semantic retrieval, but poorly designed implementations can actually reduce recall and hide relevant results. Learn the most common pitfalls and how to avoid them.

Jun 17, 2026 5m read 👁 15