RAG systems can cut inference costs 6x by filtering out cases before reaching the LLM

Key takeaway

• Most teams building RAG classification systems route every ambiguous case directly to a language model, but in regulated industries where decisions must be auditable and defensible, this approach becomes costly and risky.

• A cascade architecture that filters out cases before they reach the LLM can cut inference costs by 6x, forcing a rethink of how enterprises design these systems when probabilistic answers are not acceptable.

3 Key Points

Context & Analysis

FAQ

What is a RAG system?

Retrieval augmented generation (RAG) is a classification system that retrieves relevant context and passes it to a language model to make decisions. In most current implementations, every ambiguous case is routed to the LLM to decide.

Why does the standard RAG approach fail in regulated settings?

When decisions must hold up to scrutiny from auditors or regulators long after they are made, routing everything to an LLM and trusting it to sort out retrieved context does not work — a decision has to be explainable and defensible, not merely probabilistic.