If You Can Write Acceptance Criteria, You Can Write an AI Routing Policy

The article on AI routing policies introduces a framework for designing intelligent routing systems that direct requests to appropriate models based on defined criteria. This approach is highly relevant for local LLM practitioners running multiple models simultaneously who need to optimize for factors like latency, accuracy, cost, and capability match.

Routing policies enable more efficient resource utilization in self-hosted environments by matching tasks to the most appropriate model—whether that's a small, fast model for simple queries or a larger capability model for complex reasoning. By using acceptance criteria as the foundation, teams can systematically define which tasks should route to which models, creating reproducible and maintainable deployment strategies.

This pattern becomes increasingly important as practitioners move from single-model setups to sophisticated multi-model systems that can provide both responsiveness and capability across diverse workloads.

Source: Hacker News · Relevance: 7/10