Synthetic LLM enables the distribution of tasks across various LLMs

Synthetic LLM is a groundbreaking feature introduced to enhance task distribution across multiple Large Language Models (LLMs) from various providers by utilizing a robust weight-based load balancing system. This system ensures tasks are allocated according to predefined weights, providing a predictable and controlled distribution method, and includes an automatic failover mechanism that redirects tasks to the next LLM in case of failure, ensuring reliability. Its applications are broad, ranging from benchmarking and engine testing to cost optimization, as users can route tasks to the most efficient LLMs without compromising quality. Looking forward, plans include further refining this approach to improve decision-making by dynamically adjusting LLM priorities based on performance and selecting the best outputs through a high-quality LLM, promising enhanced results and pushing the boundaries of AI technology.