Enterprises are measuring the wrong part of RAG

Enterprises have moved quickly to adopt RAG to ground LLMs in proprietary data. In practice, however, many organizations are discovering that retrieval is no longer a feature bolted onto model inference — it has become a foundational system dependency.

Once AI systems are deployed to support decision-making, automate workflows or operate semi-autonomously, failures in retrieval propagate directly into business risk. Stale context, ungoverned access paths and poorly evaluated retrieval pipelines do not merely degrade answer quality; they undermine trust, compliance and operational reliability.

This article reframes retrieval as infrastructure rather than application logic. It introduces a system-level model for designing retrieval platforms that support freshness, governance and evaluation as first-class architectural concerns. The goal is to help enterprise architects, AI platform leaders, and data infrastructure teams reason about retrieval systems with the same rigor historically applied to compute, networking and storage.

Retrieval as infrastructure — A reference architecture illustrating how freshness, governance ...