Explores the deterministic nature of legal intelligence within systems like CLUE, contrasting them with standard probabilistic AI models. These sources argue that traditional sensitivity tuning is inappropriate for legal workflows because it introduces subjective bias into a system meant to reflect the unbiased truth of a record. Instead of altering the underlying data to reduce high volumes of evidence, the system utilizes mathematical ranking and relative scoring to prioritize information. This approach ensures that all evidence remains traceable and defensible while providing users with layered exposure to the most significant findings. Ultimately, the text reframes the challenge of high information density as a presentation problem rather than a detection flaw. The system functions as a microscope that preserves the entire evidentiary record while allowing attorneys to filter their view based on specific needs.