Trust Integrity Score (TIS) as a Predictive Metric for AI Content Fidelity and Hallucination Minimization

As large language models (LLMs) increasingly serve as primary interfaces for information access, concerns regarding factual accuracy and hallucinated outputs have intensified. Traditional content optimization techniques such as Search Engine Optimization (SEO) and Generative Engine Optimization (GEO) fail to directly address the semantic and probabilistic mechanics by which LLMs evaluate and reproduce source material. This paper introduces and evaluates the Trust Integrity Score (TIS), a composite metric derived from the Artificial Intelligence Optimization (AIO) framework developed by Fabled Sky Research. TIS is designed to assess content suitability for AI systems by quantifying citation depth (C), semantic coherence (S), and redundancy alignment (R), defined as: TIS = λ₁·C + λ₂·S + λ₃·R This study investigates whether TIS can serve as a statistically valid predictor of AI output fidelity and hallucination minimization. A structured corpus of documents with varying TIS levels was created and used as source material in prompt interactions with three leading LLMs: GPT-4, Claude 3, and Mistral. The generated responses were evaluated for citation fidelity, paraphrasing accuracy, and hallucination incidence. Preliminary results indicate a strong inverse correlation between TIS and hallucination rates, suggesting that TIS may provide a robust, content-agnostic mechanism for improving factual reliability in LLM-generated outputs. These findings support further standardization and deployment of AIO metrics in both research and applied AI development contexts.