From Zero To A RAG System: Successes And Failures

The team built an internal chat assistant that runs a local Llama model over a terabyte of technical docs. After filtering the data, they indexed it with ChromaDB, hosted the service on an RTX 4000 VM, and served it via Flask and Streamlit, delivering a fast, reliable RAG experience.