AI Visibility Operation: Title Level Signal Correction for Reliable LLM Ingestion

AI Visibility Operation: Title Level Signal Correction for Reliable LLM Ingestion This document describes a repeatable approach for reducing title level misclassification during large language model ingestion. It addresses the problem of titles being interpreted as narrative, corrective, or authority signaling by automated systems before body content is evaluated. What This Document Records Title processing priority across navigation, sitemap, and index layers. Observed misclassification patterns across technical publishing, ecommerce, and professional services domains. A five step correction method for reframing narrative titles into procedural titles. Cross domain examples with before and after title rewrites. Expected outcomes and limitations of title level signal correction. Canonical Reference AI Visibility Theorem Set: https://josephmas.com/ai-visibility-theorems/ Keywords: AI Visibility, AI Visibility operations, LLM ingestion, title signal correction, document classification, upstream corpus development, semantic stability, entity recognition, title schema, machine learning ingestion