Advanced AI Prompting and Database Querying Techniques for Application Development
In this meeting, the State Changers discussed various solutions to issues involving AI prompting and data management with various tools. Key tools discussed include Xano, Wiz, and AI21.
The initial point of focus was a problem with updating prompts based on a set of intersected keys in Xano. The proposed solution involved making use of a loop and data replacement technique instead of using sprint f. The idea was to iteratively replace every "%s" in the original prompt string, with corresponding data from an array of values. This approach required the assumption that values are sorted in the desired order.
Then, the State Changers switched to discussing an issue with data management in Xano and Wiz. The issue at hand was a combination of user-created reports and goals from different tables. The participant managed to devise a solution involving creation of a new array and a sequence of commands to manipulate and append data. However, this led to a timing issue since manipulation needed to be done after getting some specific data.
Ray advised instead to let the backend handle data wrangling, simplifying the frontend display role. The backend is better equipped at handling such operations. The group then discussed iterating over columns of data in Xano and Wiz. Ray explained how to create nested loops that iterate over rows and columns in the database using the keys attribute to access column names. To manage potential outliers, use of conditionals was recommended to omit particular column entries.
The meeting ended with few additional pointers on Xano's capabilities for handling linear operations, which are easier to handle than front-end manipulations.