At 10 PM, my product manager dropped 200 PDFs in my lap: “We need to demo an internal knowledge base Q&A for the boss tomorrow morning—super urgent.” I thought, “RAG? I know this; LangChain plus a vector database, done in minutes.” I started coding at 4 PM and barely finished by 10 PM—not because the pipeline didn’t run, but because two subtle traps dragged the accuracy below 40% and had me debugging for six straight hours. In this article, I’ll walk you through the full RAG system build and pull the two pitfalls out by their roots. Why you can’t just dump documents into GPT The simplest idea for a system that answers questions like “What is the company holiday policy?” or “What were the conclusions of project X’s retrospective?” is to concatenate all the documents into one giant prompt and send it to GPT. Reality hits fast: 200 PDFs add up to over 800,000 characters. Even GPT-4’s 128K context window chokes, and the per‑call cost will make your finance team come after you.…