RAG routine: complete definition in AI for SMEs

The RAG routine (Qlari method) is a structured, recurring approach that keeps a RAG system healthy — that is, an AI assistant connected to your documents. A RAG system is not "installed once and forgotten": it degrades when answers become wrong, documents go stale or new use cases appear. The routine organizes this maintenance on a defined cadence, with clear acceptance criteria.

What it changes for an SME

Without a maintenance routine, a RAG assistant drifts within weeks:

• responses become outdated when your procedures, prices or contracts change and indexed documents are not refreshed;
• users lose trust after two bad answers and abandon the tool;
• nobody knows who updates which sources, or how often.

The method sets up a simple, sustainable cycle:

• a cadence (weekly, monthly depending on source importance);
• owners identified per document domain;
• a reusable test set that verifies key answers stay correct;
• a failure log: every reported error becomes a permanent test case.

Why it is strategic

The quality of a RAG assistant depends less on the chosen model than on the regularity of its maintenance. A simple routine, applied monthly, catches what the technical solution does not: keeping the tool aligned with your real data. It is a concrete deliverable of fractional AI leadership — not a technology, a discipline.