Cutting RAG inference costs 6x starts with deciding what never reaches the LLM

The article argues that high-stakes RAG classification should not send every case to a language model, especially in regulated environments where decisions must be explainable months later. A cascade design handles clear, rule-based cases first and reserves the LLM for genuinely ambiguous decisions, improving auditability, latency, and cost.

This reflects a broader shift from LLM-first architectures toward hybrid AI systems that combine deterministic logic, retrieval, and model-based judgment. As enterprises move AI into compliance-heavy workflows, defensibility and cost control may matter as much as raw model capability.