Adapting content for AI: Improving accuracy of RAG solutions

Retrieval augmented generation (RAG), is one of the most common generative AI solutions that corporations are implementing. RAG is a framework that forces the large language model (LLM) to answer questions based on the documents you feed it to improve the accuracy of the answers. However, most of the information I’ve found on implementing RAG assumes that if you set up the framework properly, the LLM will automatically generate accurate and complete answers. But what about considering how the actual content of the documents might affect the solution?