NadirClaw Tutorial Shows How to Route Prompts to Gemini and Cheaper Models to Cut LLM Costs

A practical tutorial walks developers through using NadirClaw to classify prompts locally and route them to the best model — sending complex queries to Gemini while keeping simple tasks on cheaper models — to reduce LLM API spend. The guide uses a local‑first approach so teams can test classification and routing logic without making live LLM calls, and it explains how to enable live routing later; this matters because it lets teams iterate safely and conserve costly API usage.

The article opens with environment setup and installation steps, showing how to install packages such as nadirclaw, openai, sentence — transformers, scikit — learn, pandas and matplotlib. It demonstrates capturing a Gemini API key from the environment or a hidden prompt for later live routing sections, and provides a classify() wrapper that invokes the nadirclaw CLI and returns JSON. Example prompts range from trivial tasks ("What is 2+2?") to complex developer requests ("Refactor the auth module...") so readers can see how classification behaves across cases.

The tutorial details how to embed prompts and inspect centroid vectors that drive routing decisions, including visualizations of similarity scores and examples of how centroid inspection informs threshold choices. It walks through experimenting with confidence thresholds and shows how those thresholds change whether a prompt is handled locally or escalated to a larger model, giving concrete ways to tune precision versus recall in routing.

On the routing side, the guide explains launching a NadirClaw proxy server and sending OpenAI‑compatible requests through it. It demonstrates switching between cheaper models for simple prompts and routing complex ones to Gemini, and it provides a reproducible method to compare routed model behavior and estimate cost savings against an always‑Pro baseline.