Generative machine learning model for personalized knowledge session content

A system comprising:

• one or more computer processors;
• one or more computer readable storage media;
• and
• program instructions stored on the one or more computer readable storage media for execution by at least one of the one or more computer processors, the program instructions comprising instructions to:
• processing user profile data of a plurality of users and service description data that is descriptive of a service;
• assigning, based on the user profile data and the service description data, the plurality of users to a plurality of user groups;
• generating a personalized message for each user group based on the user profile data and the service description data, wherein the personalized message is generated using one or more machine learning models;
• providing each personalized message to each user group and collecting feedback data based on the providing of each personalized message;
• analyzing, using a calibration model, the feedback data to identify one or more capabilities that are lacking in the service, wherein the one or more capabilities include a code module, a protocol, or a configuration setting;
• and
• adding the one or more capabilities to the service.

One or more non-transitory computer readable storage media having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform operations including:

• processing user profile data of a plurality of users and service description data that is descriptive of a service;
• assigning, based on the user profile data and the service description data, the plurality of users to a plurality of user groups;
• generating a personalized message for each user group based on the user profile data and the service description data, wherein the personalized message is generated using one or more machine learning models;
• providing each personalized message to each user group and collecting feedback data based on the providing of each personalized message;
• analyzing, using a calibration model, the feedback data to identify one or more capabilities that are lacking in the service, wherein the one or more capabilities include a code module, a protocol, or a configuration setting;
• and
• adding the one or more capabilities to the service.