The Feature Store Pattern for LLM Applications: Stop Retrieving What You Could Precompute
· 10 min read
Most teams building LLM applications eventually converge on the same ad-hoc architecture: a scatter of cron jobs computing user summaries, a vector database queried fresh on every request, a Redis cache added when latency got embarrassing, and three different codebases that all define "user preference" slightly differently. Only later, usually after a production incident, do they recognize what they built: a feature store — a bad one, assembled accidentally.
The feature store is one of the most battle-tested patterns in traditional ML infrastructure. Applied deliberately to LLM context assembly, it eliminates the latency, cost, and consistency problems that plague most retrieval pipelines. This post explains how.
