The goal of the engagement was to demonstrate that we could reduce LLM operating costs and improve RAG efficiency without compromising output quality. A technical evaluation confirmed that inefficient retrieval was the primary cost driver, as unnecessary context was consistently being passed into the model. To solve this, the approach focused on optimizing how documents were segmented, retrieved, and assembled within the RAG pipeline, placing a strong emphasis on precision in retrieval. The document chunking strategy was redesigned to ensure content was broken into smaller, semantically coherent chunks, improving retrieval accuracy and relevance. Retrieval logic was then refined to prioritize high-quality, contextually-relevant matches over volume, ensuring only the most relevant information was selected for each query. In parallel, prompt construction was optimized to eliminate redundant, low-value content. This further reduced token usage while maintaining consistent output quality. Together, these improvements aligned the system with modern enterprise RAG best practices, ensuring the LLM received focused, high-signal inputs instead of large, unfocused datasets. By restructuring the flow of data through the RAG pipeline, the system became more efficient, more cost-effective, and better aligned with scalable enterprise AI design principles.