In the meeting, State Changers discussed how to use Xano for their model and analyzed a back-end workflow from this application. They delved deep into practical implementation and showcased how one can make API calls to fetch necessary information, which was further processed using Xano. This involved filling a prompt with the required data and getting a response back via an API call.
They also emphasized troubleshooting potentially incorrect results by modifying the prompt, depending upon the outcome. An essential aspect presented was the challenge of dealing with lists, leading to a recommendation to use JSON format for more structured responses. After this, an interactive demo was presented, utilizing percent s and percent 1 in creating prompts. The team used a variable style prompt for more efficient outcomes and advised that any discrepancy between expected and actual model outputs should be addressed by rerunning the code. Overall, the meeting offered practical advice on implementing Xano in their stack, troubleshooting techniques, optimizing model predictions and making data exchange more orderly using JSON. No other mentioned keywords were discussed during this session.
(Source: Office Hours Extra - OpenAI 2/10 )
