RAG

Context Window Bloat: When Adding More History Hurts LLM Accuracy

Bigger context windows don't automatically produce better AI responses. In many cases, stuffing an LLM with excessive conversation history, documents, or retrieved passages reduces answer quality, increases latency, and introduces distractions. Learn why context window bloat occurs and how to keep

Jul 26, 2026 5m read 👁 0

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 👁 17