Imagine a world where creating realistic, yet completely private, synthetic data for identity documents is not only possible but also incredibly efficient. Researchers are now leveraging the power of Large Language Models (LLMs) to generate synthetic data for identity documents like driver's licenses, insurance cards, and university IDs. This groundbreaking approach bypasses the limitations of traditional methods that rely on rigid templates and often fail to capture the rich diversity of real-world documents. The problem is that training AI models to detect barcodes on IDs requires massive datasets, but access to real documents is restricted due to privacy concerns. Older synthetic data generators create data that's too uniform and unrealistic, failing to reflect the many variations in document layouts and data fields across different issuers and regions. The solution lies in using LLMs. By feeding the LLM detailed prompts, researchers are able to generate contextually rich metadata that reflects the diversity found in real-world IDs. This metadata is then encoded into barcodes and embedded into document templates. The result? Highly realistic synthetic documents without any real personal information. Tests show that models trained on this LLM-generated data significantly outperform those trained on traditionally generated data. This marks a significant step forward in areas like security, healthcare, and education, where accurate barcode detection is crucial. Not only does it improve the accuracy of barcode detection systems, but it also does so while protecting privacy. This advancement paves the way for more robust and efficient automated document processing and identity verification systems. While there are challenges to address, such as the cost of running LLMs, and the need for even more robust testing on real-world data, this innovation offers a promising future for data privacy and AI-driven document processing.