This project aims to develop an automated system for efficiently collecting and pruning vast datasets required for training Large Language Models (LLMs). It will focus on creating scalable algorithms that can identify, gather, and filter relevant data from diverse sources while ensuring data quality and relevance. The system will incorporate advanced techniques in data mining, natural language processing, and machine learning to optimize the data preparation phase, crucial for training robust and accurate LLMs.