A three-stage cascade architecture cut LLM inference costs by roughly 6x in a production classification system, according to Vineet Vijay, a lead AI and ML engineer who spent a year building RAG systems in regulated enterprise environments. The core finding: routing only the genuinely ambiguous 10 to 15 percent of cases to the language model, while handling the deterministic majority with exact matches and structured field comparisons, improved consistency on the easy cases to near-perfect while slashing compute spend. Most teams skip this design entirely and send everything to the LLM. That works in a demo. It fails an audit.
The article is worth reading in full for two reasons beyond the cost number. First, Vijay makes a precise argument about retrieval quality versus generation quality that most RAG writeups collapse together: if stage two pulls the wrong context, the LLM produces a confident, well-reasoned, wrong answer, and your eval metrics may not catch it. He specifies tracking retrieval ranking scores and final classification accuracy independently, and oversampling stage-three cases in your evaluation set rather than mirroring production distribution, which is dominated by deterministic cases the cascade already handles. Second, he details an asymmetric risk prompt design that instructs the model to treat uncertainty as a reason to escalate, requests a confidence score alongside every classification, and uses that score as a second cascade trigger to route low-confidence outputs to human review regardless of the model's answer.
The closing argument is architectural, not motivational: cascade design is not a workaround for LLM limitations, it is what a mature RAG system looks like after its decisions have been challenged by a regulator or compliance officer. Vijay also describes a feedback loop where human reviewer overrides get written back into the retrieval corpus, so the system's handling of ambiguous cases actually improves over time instead of repeating the same category of mistake. The question he ends on is the right one to start with: which parts of this decision should never have been the model's job.
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