ユーザーにLlmモデルを選ばせるべきではない理由：「選択」が常にベストとは限りません

The text discusses the challenges and inefficiencies of allowing users to choose from a dropdown of large language models (LLMs) in AI platforms, arguing that this approach often leads to suboptimal results due to the lack of user expertise in selecting the most appropriate model for specific tasks. It emphasizes that model selection should not be based on user preference or the latest releases but should be a system-level optimization problem informed by data and continuous evaluation. The text advocates for a dynamic, data-driven routing system where the appropriate model is automatically selected based on task-specific requirements, performance metrics, and historical data, thereby improving consistency, efficiency, and output quality. This approach shifts the focus from user-led selection to orchestrated optimization, enabling users to consistently receive high-quality results without needing to understand the complexities of model selection.