Up to 80% of enterprise AI projects fail in production. Not because the AI is dumb, but because developers treat it like it has a photographic memory. It doesn't. Imagine taking a highly technical exam closed book. You rely on what you memorized months ago, panic, and confidently make things up. Now, imagine taking it open book with a fast assistant who runs to the library, grabs the exact page you need, and hands it to you. That is rag. Retrieval augmented generation doesn't train the AI. Its weights never change. You are literally just copy-pasting relevant text files into the prompt window right before the model reads it. RAG doesn't make the AI smarter. It just gives it a search engine. But here is the trap. If your data parser turns a beautifully formatted PDF table into a messy, unreadable string of text, no LLM on Earth can read it. Junior devs spend all their time tweaking prompts. Seniors know that RAG is a data engineering problem, not an AI problem. Before you write a single line of prompt engineering, clean your data. Because garbage in always means garbage out.
Machine-transcribed from the published render, so acronyms may be cased as ordinary words.