Personalized intent automation using dynamic prompt generation for a machine learning model

A system for automated software application guidance, comprising:

• one or more processors;
• and
• a memory comprising instructions that, when executed by the one or more processors, cause the system to:
• detect that a trigger event associated with a user has occurred within a software application;
• aggregate data related to the user from one or more data sources based on recommendable actions associated with the trigger event;
• generate a prompt for a machine learning model based on the aggregated data, the recommendable actions, and scores associated with the recommendable actions;
• provide the prompt to the machine learning model;
• receive, from the machine learning model in response to the prompt, a recommended action of the recommendable actions;
• display an indication of the recommended action via the software application;
• receive feedback from the user with respect to the recommended action;
• and
• update one or more of the scores associated with the recommendable actions based on the feedback.

A non-transitory computer readable medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to:

• detect that a trigger event associated with a user has occurred within a software application;
• aggregate data related to the user from one or more data sources based on recommendable actions associated with the trigger event;
• generate a prompt for a machine learning model based on the aggregated data, the recommendable actions, and scores associated with the recommendable actions;
• provide the prompt to the machine learning model;
• receive, from the machine learning model in response to the prompt, a recommended action of the recommendable actions;
• display an indication of the recommended action via the software application;
• receive feedback from the user with respect to the recommended action;
• and
• update one or more of the scores associated with the recommendable actions based on the feedback.