The text message that originates from the front-end is then transmitted to the ChatGPT back-end, which was built on the foundation of CallingGPT. This allows a Python function documented in Google style docstring to be converted into a JSON format recognizable by ChatGPT, which can then be invoked whenever ChatGPT finds it necessary. Furthermore, it closes a feedback loop between ChatGPT and the local Python function, as the suggested function will be immediately executed locally and its return value will be sent back to ChatGPT. One obstacle we faced was that some tasks required the execution of perhaps three functions consecutively. However, ChatGPT didn't always complete all the steps; it might return an incomplete or even failure message after executing one or two functions. To enhance the robustness of function chain-calling, we implemented a simple trick in CRESt, which is basically adding more "prompt guidance" in the function return message. This approach turned out to be somewhat effective. Specifically, ChatGPT receives one of three templates after it calls a function: