Language models have transformed automated text generation by enabling systems to generate coherent, contextually relevant content across diverse domains. However, when faced with domain-specific tasks that require the precise use of specialized terminologies, these models frequently encounter challenges in tokenization and knowledge retrieval. The novel combination of token factorization and retrieval-augmented generation (RAG) presents a significant advancement, offering a more accurate and contextually informed generative process. Token factorization improves the representation of complex terms through the decomposition of tokens into smaller, semantically meaningful units, which significantly enhances the model's ability to generate domain-specific text with precision. Simultaneously, the integration of RAG allows the system to access and incorporate external domain-specific knowledge dynamically, ensuring that the generated content remains factually accurate and relevant. Through extensive experimentation, the research demonstrates substantial reductions in error rates, improvements in retrieval accuracy, and increases in fluency when these techniques are applied to specialized domains such as legal, medical, and technical fields. Although the enhancements introduce additional computational complexity, the overall improvements in performance justify the trade-offs. The findings contribute to the broader application of language models in domains requiring high accuracy and contextual depth, paving the way for more effective text generation systems in specialized industries.