Meta's engineering team describes a hybrid pattern for privacy-aware infrastructure (PAI) asset classification at scale. The core approach combines LLMs for handling ambiguous or novel data assets with deterministic, versioned rules for routine enforcement. Key principles include: building structured 'evidence briefs' from distributed context (code lineage, ownership, semantic annotations) rather than raw fields; keeping human-reviewed labels separate from model-generated recommendations; using a multi-panel LLM judge with Cohen's kappa for quality control; and progressively distilling stable LLM-discovered patterns into auditable deterministic rules. In production, ~85% of traffic is resolved by fast deterministic rules, with LLM fallback for the remaining ~15%. The system includes self-regulation mechanisms (tuning controller states) to prevent runaway optimization loops. Lessons learned emphasize that context quality beats prompt quality, accuracy alone is insufficient for imbalanced taxonomies, and distillation into deterministic rules is the sustainable production model.
