Create an intelligent routing system that learns to direct each request to the most appropriate model based on multiple factors including input complexity, quality requirements, and resource constraints. This system combines a sophisticated classifier trained on your domain-specific data with explicit cost-quality-latency preferences to make optimal routing decisions. The router learns from both historical performance data and your scoring system to understand which models excel at different types of requests, enabling it to make increasingly accurate routing decisions over time.
Effective model routing is crucial for building production AI systems that can handle diverse requests while optimizing for multiple competing constraints. By mastering routing techniques, you'll learn to create systems that automatically balance quality, cost, and latency based on your specific requirements. The router's ability to learn from your scoring system means it continuously improves its decision-making, leading to more efficient resource utilization and better overall system performance.
Implement routing when you have access to multiple models (both general-purpose and specialized) and need to optimize their usage based on varying request characteristics and requirements. For example, in a content moderation system, you might route simple text-based cases to efficient specialized models while directing complex multi-modal cases to more powerful general-purpose models. The router can learn that certain models perform better on specific content types or languages, using your scoring system to validate these decisions. This technique is particularly valuable when working with a mix of public and private models, each with different cost structures and specializations. Consider implementing routing when you need to automatically balance multiple competing factors like cost constraints, latency requirements, and quality thresholds across a diverse set of use cases.