Thank you. One of main features of ROScribe (which is still under development) is the use of RAG (Retrieval Augmented Generation) for robot software generation. In RAG, the code, rather than being generated by the LLM, is pulled from ROS index by LLM (LLM keeps all documentations and meta data of ROS index in a vector data base to figure out what piece of code is most suitable for the task at hand.) We are working on this part and we plan to publish a conference paper on it. So the ultimate solution is a mixture of codes pulled from online repositories (ROS index), internal data (companies own IPs), and the code that LLM generates. LLM is handling all of three segments, but only codes as minimum as needed.