How to Build a Cost-Aware LLM Routing System with NadirClaw Using Local Prompt Classification and Gemini Model Switching

The tutorial you've referenced provides an in-depth guide to building a cost-aware Large Language Model (LLM) routing system using NadirClaw, which is designed to optimize the use of different AI models based on prompt complexity and other factors. Here's a summary of key points covered:

1. Introduction to NadirClaw: This section introduces NadirClaw as an open-source tool that helps route prompts between different LLMs (like Gemini Flash and Pro) based on their complexity, ensuring efficient use of resources.

2. Setup Environment: The tutorial guides you through setting up the necessary environment for running NadirClaw locally, including installing required Python packages and configuring API keys.

3. Local Prompt Classification: It explains how to classify prompts locally before sending them to an AI model. This involves using a machine learning model trained on prompt complexity features to predict which model (e.g., Gemini Flash or Pro) would be most suitable for the given task.

4. Centroid-Based Similarity Explanation: The tutorial delves into how NadirClaw uses centroid-based similarity measures to explain routing decisions. It helps in understanding why certain prompts are routed to specific models based on their complexity characteristics.

5. **Threshold Tun

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