This research explores how to move beyond monolithic LLM architectures by implementing routing strategies that direct queries to the most appropriate components.

• Resource Optimization: Route simpler queries to smaller, specialized models to reduce computational costs
• Performance Enhancement: Direct complex questions to more capable models only when necessary
• System Flexibility: Create adaptable architectures that can evolve with changing requirements
• Cost Efficiency: Achieve better results with fewer resources through intelligent distribution

For engineering teams, this approach offers a practical framework to build more efficient LLM-based systems that balance performance needs with resource constraints.

Doing More with Less -- Implementing Routing Strategies in Large Language Model-Based Systems: An Extended Survey