AI Hallucinations and Observability: A Unified Framework for Detection, Mitigation, and Self-Aware AI Systems

Abstract

Artificial Intelligence systems, especially Large Lan- guage Models (LLMs), have shown rapid progress in generating and understanding human language. Despite these advancements, hallucination—where models produce incorrect or unsupported information—remains a critical challenge. Existing solutions often focus on individual aspects such as detection or correction, but lack an integrated approach.

This paper proposes a unified observability-driven framework that integrates memory systems, generalization boundary de- tection, cross-model validation, and continuous monitoring. The proposed system enables AI to become self-aware by learning from past behavior and identifying its knowledge limits. The framework improves reliability, reasoning consistency, and trust- worthiness of AI systems.