Imagine an AI that can understand any document, from a complex legal contract to a simple grocery receipt, without needing explicit training on each document type. This is the promise of open-world document understanding, and researchers are making significant strides towards achieving it. A new technique called DocKD (Document Knowledge Distillation) leverages the power of large language models (LLMs) like Claude-2 to teach smaller, more specialized AI models how to extract information from diverse documents. The traditional approach of directly using LLMs for document understanding faces challenges. LLMs often struggle with the unstructured nature of text extracted from documents through Optical Character Recognition (OCR). The text lacks the formatting and layout cues that humans naturally use for comprehension. DocKD overcomes this by adding external document knowledge into the mix. It feeds the LLM not just the raw text, but also crucial information about the document's layout, key-value pairs, and even short descriptions. Think of it as giving the LLM the context it needs to understand the "bigger picture." This enriched information allows the LLM to generate high-quality, structured data, like question-answer pairs for a document or potential entity fields. This generated data is then used to train the smaller, specialized document AI model. The results? These smaller models, trained only on the synthetic data from the LLM, achieve performance comparable to models trained on human-annotated data for specific tasks and even surpass them in handling new, unseen documents. This has profound implications. DocKD opens doors to building versatile document AI systems that can adapt to any document type without extensive manual labeling. It democratizes access to sophisticated document understanding technology, making it more affordable and adaptable for diverse applications. However, challenges remain. The current research primarily focuses on common document types and simpler tasks. Extending this approach to more visually complex documents, like those with diagrams or scientific notation, will require further innovation in how external knowledge is integrated and how LLMs are prompted. Despite these hurdles, DocKD marks a crucial step toward truly versatile, open-world document understanding, hinting at a future where AI can seamlessly interact with and extract information from the vast world of documents surrounding us.