Truthfulness Scoring Models for Knowledge-Grounded AI Systems

Abstract

This article presents a structured framework for truthfulness scoring in knowledge-grounded AI
systems, aimed at improving factual reliability, output consistency, and interpretability in data-driven
applications. The proposed approach integrates a multi-layer verification pipeline that retrieves
authoritative knowledge, constrains generative reasoning, and evaluates the semantic coherence of
model outputs before presenting them to users. The methodology emphasizes alignment between the
inference process and validated enterprise data, ensuring that generated responses remain anchored to
verifiable information sources. Results indicate a significant reduction in hallucination tendencies and
an increase in user trust when truthfulness scores are displayed alongside AI-generated outputs. The
scoring system also demonstrates adaptability over time, maintaining relevance as organizational
knowledge and workflows evolve. The findings highlight the importance of embedding truthfulness
scoring as a core architectural component rather than a peripheral validation step in modern AI
applications.