This tutorial demonstrates how to implement an intelligent routing layer using NadirClaw to optimize Large Language Model (LLM) costs. The system classifies prompts into simple or complex tiers locally before selecting the most appropriate model, such as switching between Gemini Flash and Pro versions. It covers installation, local classification testing via CLI, visualizing decision boundaries through centroid-based similarity scores, running a proxy server for live routing, and calculating estimated cost savings compared to using high-end models exclusively.
