LLM Council works together to answer your hardest questions. A local web app that uses OpenRouter to send queries to multiple LLMs, have them review/rank each other's work, and finally a Chairman LLM produces the final response.
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.
