Imagine trying to teach a computer to understand the information locked within a chart – the rising lines, the colorful bars, the scattered points. It's a complex task that demands not just recognizing the visual elements, but also understanding the relationships between them and reasoning about the underlying data. Researchers are constantly striving to improve how AI interprets these visual representations of data, and a new approach called 'SBS Figures' is making significant headway. Traditional methods for training AI on charts often rely on painstaking manual annotation of real-world charts or generating synthetic charts with limited variation. However, SBS Figures, which stands for Stage-by-Stage Synthesized Figures, offers a more efficient and scalable solution. This innovative pipeline automatically generates a massive dataset of synthetic chart figures, each accompanied by detailed annotations of the visualized data and a rich set of question-answer pairs. The magic lies in the stage-by-stage generation process. First, the pipeline generates a diverse range of data topics, from business and marketing to health and social trends. Next, it automatically creates the corresponding chart data in a structured JSON format. This JSON data then feeds into pre-defined Python scripts that render the charts, introducing variations in font, title placement, legend position, and other visual elements. Finally, large language models (LLMs) generate relevant questions and answers based on the underlying JSON data, eliminating the need for optical character recognition (OCR) and ensuring accuracy. The result is a dataset of over one million synthetic chart images, paired with over four million question-answer pairs. This vast dataset is diverse, accurate, and free from copyright restrictions, making it a powerful tool for pre-training AI models. Experiments show that models pre-trained on SBS Figures significantly outperform models trained on other synthetic datasets or from scratch when fine-tuned on real-world chart question-answering tasks. They are even comparable to models trained with expensive real world annotations. This research opens exciting new avenues for developing more robust and intelligent AI systems capable of understanding and reasoning about the wealth of information conveyed through charts. From automating data analysis and report generation to powering interactive data visualization tools, the potential applications are vast. While further improvements are possible, including generating even larger datasets and refining the hyperparameters, SBS Figures represents a significant leap forward in the quest to teach AI to see and understand the world of charts.