Retrieval-augmented generation (RAG) systems have emerged as a powerful approach to enhancing the performance of Large Language Models (LLMs) by integrating them with external knowledge sources. However, existing RAG systems face challenges in cost efficiency, retrieval efficacy, and the effective utilization of retrieved information. This report proposes an innovative methodology for optimizing the preprocessing pipeline of RAG systems, focusing on parsing, content refinement, and dynamic content-aware chunking. The proposed techniques aim to reduce token usage, improve attention score distribution, and enhance the semantic coherence of the knowledge base while preserving its informational integrity. The results demonstrate a significant reduction in token count while retaining a high semantic similarity between the original and refined knowledge base and enhanced retrieval efficacy on the PubMedQA dataset. The resulting chunks also displayed denser and more evenly distributed attention scores, indicating increased generative capability for large-context applications. These results are based on extensive evaluation metrics that assess different RAG processes. This research could serve as a basis for a comprehensive review of cross-domain applicability or impact analysis of similar preprocessing methods to develop more efficient and reliable RAG systems, promoting widespread adoption in various domains.