This paper explores the integration of large language models (LLMs) with continuous data streams, addressing the challenges and methodologies associated with this integration. It examines two primary approaches: continual learning and prompt engineering. Continual learning allows LLMs to adapt to new data inputs through ongoing pre-training, instruction tuning, and alignment while mitigating issues like catastrophic forgetting with replay-based and regularization-based techniques. Prompt engineering, on the other hand, transforms real-time data into model-readable inputs, enabling dynamic responses based on contextually relevant prompts. The paper highlights the workflow of LLMLight for traffic signal control and the Graph Transformer-based Traffic Data Imputation (GT-TDI) model for traffic data imputation, demonstrating the practical applications of these approaches. Despite the progress, challenges such as the lack of transparency in model training processes and limited real-world applications persist. Future work should focus on optimizing computational efficiency, automating prompt generation, and expanding the practical use of LLMs in diverse real-world scenarios.