Building a production-ready Retrieval-Augmented Generation (RAG) architecture such as an enterprise internal knowledge assistant requires balancing data persistence, vector math, and asynchronous network loops. While the high-level concepts of document ingestion and language model synthesis are straightforward, engineering a performant system introduces systemic complexities. Below is an engineering analysis of the primary technical challenges encountered when implementing this architecture, ...