The network measurement and management are more challenging in the next generation network systems due to the increasing demand for communications and complex network infrastructure. The emerging artificial intelligence (AI) algorithms have recently attracted much attention in networking systems, such as AI-based network traffic classification, traffic prediction, intrusion detection system, etc. The development and maintenance of networking AI models usually require many traffic data samples from real applications. However, there are only a few such datasets. In this paper, we develop a real application enabled traffic generator for AI model development in networking. In particular, a data loader is provided to establish two databases. One is a payload database consisting of packets from real applications. The other one is a traffic database that consists of network traffic patterns that follow real applications. The traffic generator allows a user to generate data traffic that mimics a mixture of real applications. Reconfigurability is also provided for arbitrary traffic generation. Evaluation is conducted by developing two networking AI models based on simulated traffic. The testing on real network traffic demonstrates that the developed traffic generator can help networking AI model development.