Large language models, such as GPT-4, LLaMA, and PaLM, are advanced tools capable of generating human-like text and providing insights, but they are limited by the data they were trained on and can struggle with untrained questions, sometimes leading to incorrect or "hallucinated" answers. To enhance their accuracy, particularly when dealing with complex documents like PDFs, a method has been developed to make such static formats more interactive by splitting the documents into manageable sections, creating embeddings, and storing them in a vector database. This approach allows the models to reference specific, relevant sections of a document when responding to queries, thereby improving the accuracy of the answers and mitigating the risk of hallucinations. An example of this process is demonstrated using documents from the International Crisis Group, where the system efficiently identifies and retrieves information relevant to specific queries, such as finding documents about terrorism or identifying individuals like Saefuddin Zuhri, and even plotting geographical locations on a map. This innovation not only overcomes the input limitations of large language models but also enhances their utility in research and accessibility.