Vision-Language-Action (VLA) models are transforming robotics by enabling systems to understand and execute tasks through video data enriched with temporal captions. These captions, which describe the sequence and progression of actions in a scene, are crucial for training VLA models but traditionally require extensive manual effort to create. Encord leverages GPT-4o, a multimodal AI with strong temporal reasoning, to automate caption generation, significantly reducing the time and resources needed for labeling. By integrating GPT-4o within Encord's structured workflow, teams can automatically produce consistent and structured captions, which are then refined by human reviewers, creating a more efficient and precise dataset development process. This automation not only accelerates the creation of datasets but also frees up teams to focus on tasks that enhance performance, such as model experimentation and real-world data collection, ultimately leading to more effective VLA models.