Discussing Dynamic API Calls and Ensuring Data Accuracy in AI-Based Applications
This meeting among the State Changers focused on discussing prompt and response mechanisms for API calls in their application, particularly instances where the output doesn't match the expected result. The State Changers suggest that an insertable variable could optimize the response to the selected number of bullet points needed in a response. They believe that this could make the application more dynamic and adaptive in its responses.
Discussions then veered into the inconsistencies in the format of data retrieved from the API calls, with the returned data occasionally not structured correctly or not matching the specified format. Some proposed solutions involve implementing wrappers to verify data before progressing, which prevents system failure due to improperly formatted data.
A major insight from the conversation was the unique nature of AI-based technologies, which are statistically driven rather than algorithmically. This introduces an element of unpredictability or randomness that requires additional checks compared to traditional API-based systems. This could also shape how future applications integrate and interact with AI-based systems. Unfortunately, the terms stated (Xano, WeWeb, FlutterFlow, etc.) were not explicitly included or discussed in the meeting.