The BAIR blog outlines a framework that quantifies LLMs interactions at scale. It combines feature attribution, data attribution, and mechanistic analysis to map how inputs influence hidden states. Practitioners can use these maps to spot brittle behaviors and design safer prompts. The approach scales to millions of token interactions, offering a practical diagnostic tool for model developers.