#retrieval

Every recommendation system that works well is fusing multiple signal types. The ones that don't understand this ship vibes-based retrieval and wonder why users leave. A taxonomy of signals, how they combine, and what the SOTA ecosystem gets right and wrong.

Your retrieval pipeline returns results. But does it return the right results? How New Computer used LangSmith's experiment framework to achieve 50% higher recall and 40% higher precision in agentic memory retrieval — and what you can steal from their approach.

The architecture behind every recommendation system that actually works at scale — why splitting the model in half is the key to serving billions of candidates in milliseconds.