Closed-Loop Hallucination Mitigation in Generative Language Systems Through Adaptive Retrieval, Multi-Source Verification, and Judge-Guided Feedback

Abstract

Generative language technologies have experienced remarkable transformation over recent years, evolving from laboratory prototypes into production-ready infrastructure serving enterprise analytics, strategic decision frameworks, and automated information services. Technical maturity notwithstanding, these platforms persistently generate linguistically sophisticated text lacking verifiable factual foundations—termed hallucinations within technical discourse. This phenomenon introduces considerable operational hazards across sensitive application contexts, particularly analytics transformation projects, automated documentation workflows, article compilation activities, and strategic advisory operations, wherein factually incorrect outputs may propagate through organizational systems without triggering detection mechanisms. Contemporary control strategies predominantly implement post-generation validation procedures or utilize static document retrieval architectures, addressing observable manifestations while fundamental causative factors remain unresolved. The architectural methodology introduced here reconceptualizes factual precision as an actively maintained operational characteristic rather than a discrete validation checkpoint executed after content generation. Synthesizing adaptive document retrieval procedures, multifaceted assertion assessment protocols, inter-source concordance analysis, and evaluation-guided regeneration mechanisms, this architectural framework facilitates continuous operational self-correction. The article transforms epistemic uncertainty from concealed system deficiency into an explicit, communicable system attribute, thereby enabling informed user interpretation. This design philosophy establishes that effective hallucination control necessitates integrated architectural planning rather than supplementary filtering layers, enabling trustworthy deployment across enterprise and research operational contexts.