Self-Reflective Large Language Models for Reducing AI Hallucinations: A Novel Framework for Reliable Generative AI

Large Language Models (LLMs) have become a central technology for generative artificial intelligence, but their tendency to produce fluent yet factually unsupported information remains a major barrier to dependable deployment. This paper proposes a self-reflective framework in which an LLM generates an answer, identifies claims that may be uncertain, performs an internal verification stage, and revises the response before delivery. Unlike a single-pass generation process, the proposed approach separates generation, claim inspection, evidence-oriented verification, and response refinement. The framework is designed to reduce unsupported claims while preserving useful information and acceptable response latency. The paper presents a research-oriented evaluation methodology using factuality, unsupported-claim rate, answer completeness, calibration, and computational overhead as evaluation dimensions. The proposed framework can be integrated with retrieval-augmented generation, external knowledge sources, or domain-specific validation modules. The study argues that self-reflection should be treated not merely as prompt engineering but as a structured reliability layer for generative AI systems.