How to Build a Sovereign RAG Pipeline That Actually Works in Production
How to Build a Sovereign RAG Pipeline That Actually Works in Production I spent the last 6 months building RAG pipelines for three different clients. The first one failed spectacularly — the AI hallucinated product prices and told a customer our client's company was founded in 1987. It was founded in 2019. That mistake cost us a week of debugging and a very awkward client call. The difference between that failure and the pipeline I run now comes down to three things: data isolation, chunking strategy, and evaluation. Get those right, and RAG actually works. Get them wrong, and you've got an expensive chatbot that lies. This is the exact architecture I use for production RAG systems — the one that handles 80% of customer inquiries for my e-commerce client without hallucinating a single product detail. What "Sovereign RAG" Actually Means Most RAG tutorials show you a Jupyter notebook with 50 lines of LangChain code and a ChromaDB instance. That's a prototype. P...