Quality Context: Why AI Agents Need Better Info, Not More

What this post added

This post elaborates on the challenges of context quality in AI agents, introducing failure modes such as context poisoning, distraction, confusion, clash, and rot. It details technical solutions for improving context quality, including precision-focused re-ranking (using cross-encoders), hybrid search (combining vector search and BM25 with RRF), metadata filtering, and freshness constraints. The post argues that retrieval quality is paramount for context quality and that these techniques collectively shape the information provided to LLMs, directly impacting agent performance.