In recent years, the technological space experienced the proliferation of Generative AI models. A prominent type of this model is a language model-based chatbot. The primary function of these models is to generate answers to a question from an extensive database and create a stream of conversation at various levels of complexity. The database of these models encompasses diverse data type of text (e.g., ChatGPT), audio (e.g., PlayHT), or images (e.g., DALLE-2). The intricate process involves neural networks, which undergoes pre-training from the database, building result from architecture neural networks, refined tuning to creating coherent result, probability estimation to produce the correct context result, and generating and refinement as improvement to generated answers. This proposal aims to delve deep into the probability estimation process of the generative AI model. A specific focus is to predict an airborne object's trajectory to create an understanding of how to adapt and adjust robotic limbs and enable them to intercept and capture the thing with some degree of precision.