Show HN: Route your prompts to the best LLM
Analysis
This Hacker News post introduces a dynamic router for Large Language Models (LLMs). The router aims to improve the quality, speed, and cost-effectiveness of LLM responses by intelligently selecting the most appropriate model and provider for each prompt. It uses a neural scoring function (BERT-like) to predict the quality of different LLMs, considering user preferences for quality, speed, and cost. The system is trained on open datasets and uses GPT-4 as a judge. The post highlights the modularity of the scoring function and the use of live benchmarks for cost and speed data. The overall goal is to provide higher quality and faster responses at a lower cost.
Key Takeaways
- •Dynamic LLM router that selects the best model and provider for each prompt.
- •Improves quality, speed, and cost-effectiveness of LLM responses.
- •Uses a neural scoring function (BERT-like) to predict LLM quality.
- •Trained on open datasets with GPT-4 as a judge.
- •Balances user preferences for quality, speed, and cost.
“The router balances user preferences for quality, speed and cost. The end result is higher quality and faster LLM responses at lower cost.”