Imagine having an AI assistant that can instantly answer any customer question about your software, pulling accurate information directly from your documentation. That's the promise of Retrieval-Augmented Generation (RAG). But building an effective enterprise-scale RAG system isn't as simple as plugging in an LLM and hitting "go." In a recent research paper, experts at IBM Canada and US revealed that a key ingredient often overlooked is the content itself. They discovered that even minor tweaks to the way documentation is written can dramatically impact a RAG system's accuracy and usefulness. This isn't about complex algorithms or fine-tuning models—it's about smart content design. For example, simplifying a table or adding a summary to a long tutorial can make a world of difference to an LLM trying to extract relevant information. The researchers also highlight the limitations of standard RAG evaluation methods. Instead of relying solely on benchmarks, they advocate for a "human-in-the-loop" approach, using real user questions to test and refine their RAG systems. This not only ensures accuracy but also provides valuable insights into what users actually need. This research shows that when it comes to building truly helpful AI assistants, great content design is just as important as powerful LLMs. By focusing on clear, concise, and structured writing, businesses can unlock the full potential of RAG and transform how they support their customers.