Implement an intelligent model routing system that starts with lightweight, efficient models and only escalates to more powerful (and expensive) models when necessary. This technique uses your scoring system to make real-time decisions about whether a response meets quality thresholds or requires escalation. Each model in the cascade is chosen for specific strengths, creating a complementary sequence that balances performance, cost, and latency. The system can continuously learn from these routing decisions to eventually optimize the threshold settings and maximize efficiency (see Routing).
Understanding cascading architectures is essential for building cost-effective AI systems at scale. By learning to effectively route requests through a model cascade, you'll be able to significantly reduce costs and latency while maintaining high quality standards. The technique teaches valuable lessons about the trade-offs between model performance, computational resources, and response time that are crucial for production AI systems. It also provides practical experience in using scoring systems to make automated routing decisions.
Deploy cascading when you need to optimize the cost-performance trade-off in your AI system while maintaining quality standards. For example, in a customer service AI, you might start with a lightweight model for simple queries like business hours or return policies. If your scoring system indicates that the response quality falls below threshold (perhaps due to complex customer sentiment or multi-part questions), the request automatically escalates to a more sophisticated model. This approach is particularly valuable in high-volume applications where cost optimization is crucial, or in systems with varying complexity levels where not every request requires the most powerful model. The key is to configure your scoring thresholds to ensure that escalation happens exactly when needed – not so aggressive that you waste resources on simple queries, but not so conservative that user experience suffers.