Cascade Architecture Reduces RAG Costs to One-Sixth

Classification systems using RAG (Retrieval-Augmented Generation) that delegate all decisions to large language models (LLMs) face limitations in both cost and explainability. As a solution, a cascade architecture approach is gaining attention, which distributes processing across three stages. Based on implementation experience in regulated industries, this design can reduce inference costs to as little as one-sixth of the original.

The underlying issue stems from a structural problem in designs that use LLMs as the entry point for all processing. LLMs struggle to reproduce the rationale behind decisions, and explanations like "the model made a judgment based on context" do not satisfy regulatory authorities or compliance officers. Additionally, in systems processing tens of thousands of cases daily, invoicing LLMs for every case causes costs and response delays to scale linearly with processing volume. Furthermore, for straightforward cases that should be resolved by fixed rules, LLMs may introduce subtle variations. This undetectable inconsistency becomes a significant issue.

The first stage of cascade design is "deterministic processing." Cases that can be resolved through explicit rules—such as exact matching or structured field comparisons—are handled here without invoking LLMs. This stage can handle over half of all cases, and since all decisions are rule-based references, transparency is high from an accountability perspective.

Ambiguous cases that pass the first stage proceed to the second stage: the "retrieval layer." Here, evidence directly related to the ambiguity is searched and extracted—such as past reviewer decisions, documents explaining surface contradictions, and prior cases organizing edge cases. In this design, the accuracy of retrieval is more critical than the quality of generation. If incorrect context is retrieved, even the most sophisticated LLM risks producing plausible but incorrect answers.

Only in the third stage does the LLM finally appear. Rather than being the "front line," the LLM serves as the "final escalation point." Only cases requiring truly complex judgment—those that could not be resolved by deterministic processing or retrieval alone—reach this stage. As a result, LLM invocations decrease dramatically, leading to significant inference cost savings.

The implications of this design extend beyond cost efficiency. In heavily regulated sectors such as finance, healthcare, and law, decision reproducibility and explainability are essential requirements. Cascade architecture addresses these requirements while reducing costs, positioning it as a practical compromise. Rather than treating LLMs as universal problem-solvers, concentrating them on their strength—judgment in complex contexts—represents a noteworthy direction in enterprise AI design.

In RAG system design, the choice of "what to pass to the LLM" itself determines quality, cost, and accountability. Looking forward, the competitive advantage in AI systems will increasingly depend not on how much LLMs are used, but on how wisely the decision is made about where to use them.