While RAG adoption is progressing, are you facing challenges such as "accuracies not meeting expectations," "getting stuck at the PoC stage," or "operations not running smoothly"? Many of these problems stem not from LLM itself, but from the preprocessing of unstructured data. Furthermore, individual data processing and the proliferation of RAG environments across departments often lead to inconsistent preprocessing logic and quality, resulting in increased operational burden and inconsistent accuracy. In this video, we will organize common challenges in RAG construction and explain the necessary data processing concepts for improving accuracy, as well as key points for designing a data pipeline that "runs smoothly in the field." We will also demonstrate "Unstructured," a no-code solution for preprocessing unstructured data, as a concrete solution to these challenges.