統一的プロンプトの終焉：もはやllmモデルに互換性はありません

In the evolving landscape of large language models (LLMs), the era of interchangeable models has ended, marking a shift where selecting an LLM has become a significant product decision rather than a mere technical choice. This transformation is driven by the unique personalities and reasoning styles that each model, such as Anthropic's Claude Sonnet 4.5 and OpenAI's GPT-5-Codex, brings to applications, influencing user experiences and product dynamics. As models develop distinct characteristics and prompt structures evolve from static commands to adaptive systems, developers must engage in more nuanced prompt engineering. This involves creating modular prompt subunits that cater to the specific strengths of different models and leveraging continuous user feedback and internal evaluations to refine and align systems with user expectations and product goals. Consequently, the focus now shifts from achieving top leaderboard performance to selecting models that intuitively match the designed user experience, supported by custom metrics that assess readability, redundancy, and signal-to-noise ratio for a holistic evaluation of a model's integration into products.

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