Building a basic Retrieval-Augmented Generation (RAG) prototype is a weekend project. You pip install an orchestration library, load a small text file, and throw raw strings at the OpenAI API. But taking that prototype into production is an entirely different engineering challenge. In a real-world enterprise environment, native LLM implementations quickly break down due to three severe operational bugs: Unpredictable API token burn High inference latency The business risk of silent hallucinations To solve these specific bottlenecks, I built Nexus Knowledge Engine — a secure, fully containerized, production-ready enterprise RAG and LLMOps platform designed around strict retrieval quality gates, high-performance database indexing, and deep system reliability. :contentReference[oaicite:0]{index=0} Here is a deep dive into the architecture, design trade-offs, and engineering metrics behind the project.…