Large language models (LLMs) are often evaluated on benchmarks that rely on surfacelevel instructions, obscuring what defines highquality performance. We argue that tasks can be more precisely characterized through principles: human-readable rules that specify what matters for a good response to the task. Our study proposes a framework to automatically extract and generate task-level principles for data generation and evaluation. Using this approach, we build a benchmark of over 20K principle-aligned instances, enabling controllable data creation and fine-grained, interpretable assessment of LLMs. Experiments show that principles both improve output quality and scale evaluation beyond manual curation, offering a new recipe for principled assessment of LLM capabilities.1