model-selection

Reduce LLM latency and cost by implementing confidence-based routing to direct simple requests to small models, escalating only complex, low-confidence cases to larger models. This optimizes performance while maintaining accuracy.

LLM routing optimizes model selection by aligning task complexity with the appropriate model tier. It evaluates performance metrics to justify routing logic, ensuring cost-effective configurations for specific production tasks.

Match task complexity with the right LLM tier by analyzing task requirements and evaluation data. This capability matching aligns production feature needs with the most cost-effective and performant model configuration.

Yes, you need evaluation sets and performance metrics to justify your LLM routing strategy. These inputs validate the confidence-based escalation logic required to ensure accuracy while maintaining cost-efficiency.

The best way to stop over-provisioning expensive LLMs is to implement capability matching and routing strategies. This aligns simple tasks with smaller, specialized models to maintain accuracy while reducing latency and cost.

You should escalate a request to a larger LLM when handling complex, low-confidence cases. Confidence-based routing strategies use performance metrics to trigger this escalation, ensuring accuracy without over-provisioning simple requests.