This tutorial provides a step-by-step guide on building an LLM router to balance the use of high-quality closed LLMs like GPT-4 and cost-effective open-source LLMs, achieving high response quality while minimizing costs. The approach includes preparing labeled data, finetuning a causal LLM classifier, and offline evaluation using the RouteLLM framework.
This tutorial introduces promptrefiner, a tool created by Amirarsalan Rajabi that uses the GPT-4 model to create perfect system prompts for local LLMs.
