How to Cut Your LLM Classification Costs by 90%
This session details the Model Cascade architecture, a technique to drastically reduce LLM classification costs (by over 90%) while maintaining high accuracy (e.g., 95%+). Instead of running an expensive 'Oracle' model on every record, the cascade uses a cheap proxy model for initial predictions and only escalates to the Oracle when the proxy's confidence score is low. The optimal decision threshold ($\tau$) is found offline by simulating performance across samples.
Key takeaways
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Model Cascade Architecture
8:31
The pipeline first sends every record (e.g., support ticket) to a cheap LLM API (the proxy/nano model). It generates a prediction $P(x)$ and a confidence score $C(x)$. A decision point then determines if the score $C(x)$ is high or low, routing only low-confidence records to the expensive 'Oracle' model.
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Cost Optimization Principle
17:31
The goal is to find a threshold $\tau$ that minimizes cost while maintaining a target accuracy (e.g., 95%) relative to the Oracle model. Lowering $\tau$ increases cost savings but risks dropping below the required accuracy.
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Threshold Determination
20:48
The optimal threshold $\tau$ is found offline by simulating the cascade using a labeled sample of tickets, calculating the resulting accuracy and cost for various potential thresholds $C(x_i)$.