This report investigates the challenges and methods associated with parsing PDF documents for use with Large Language Models (LLMs). The variability and complexity of PDF formats pose significant challenges in ensuring accurate data extraction and interpretation. We evaluate several parsing techniques, including rule-based, deep learning-based, and multimodal methods, to determine their effectiveness in handling diverse PDF content. Our study reveals that while multimodal methods generally outperform others, particularly in managing mixed content formats. For PDFs containging complex images a deep learning approach is more effective. This research offers valuable insights into optimizing PDF parsing strategies, balancing accuracy and cost-efficiency, thereby advancing the utility of LLMs in document processing and contributing to improved data management practices in various industries.