Software
A production RAG system feels fast when it's consistent: answers arrive quickly, follow-ups don't suddenly slow down, and costs don't jump with traffic. The biggest lever for that isn't usually the model, it's retrieval. When retrieval is engineered like a
AI
Interactive AI experiences are different from traditional RAG pipelines. They exist inside tight user-facing latency boundaries where even a 300-500ms delay can change how users feel about the system. A chat interface that responds instantly feels intelligent, one that hesitates feels broken. When retrieval sits on the hot path of
AI
Retrieval-Augmented Generation systems rarely fail loudly. They fail in subtler, stranger ways: a support answer sounds different today than yesterday, a troubleshooting step the assistant always got right suddenly drifts, or the bot becomes strangely confident about something no one remembers teaching it. When you dig deeper, the pattern becomes