The rapid evolution of artificial intelligence has led to the development of sophisticated language models capable of generating human-like text. However, maintaining semantic consistency throughout extended text generation remains a significant challenge. Introducing Dynamic Probabilistic Contextual Realignment (DPCR), a novel methodology that dynamically adjusts contextual embeddings through probabilistic realignment, addresses this issue by mitigating semantic drift. The integration of DPCR into transformer-based large language models (LLMs) has demonstrated substantial improvements in semantic coherence and contextual relevance. Quantitative analyses reveal enhanced performance metrics, while qualitative assessments highlight more coherent and contextually appropriate text generation. The findings demonstrate the potential of DPCR to advance LLM development and applications across various domains, emphasizing the importance of dynamic context management in achieving semantic consistency.