Machine learning model prompt hydration via prompt registry and context store

A system for dynamically generating prompts, comprising:

• a data source comprising one or more data storage locations, each respective data storage location of the one or more data storage locations containing data associated with a respective use case indexed by user;
• one or more processors;
• and
• a memory comprising instructions that, when executed by the one or more processors, cause the system to:
• receive a query from a user, wherein the query corresponds to a given use case associated with a software application;
• retrieve a prompt template based on the query, wherein the prompt template comprises:
• natural language instructions related to providing a response to the queries corresponding to the given use case, and
• a placeholder comprising an indication of a given data storage location of the one or more data storage locations, wherein the given data storage location contains data associated with the given use case;
• retrieve, based on the placeholder, data associated with the user from the given data storage location;
• populate the prompt template to produce a dynamically generated prompt by replacing the placeholder with the retrieved data;
• and
• provide the dynamically generated prompt and the query to a machine learning model that has been trained to generate content in response to an input prompt and an input query.

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:

• receive a query from a user, wherein the query corresponds to a use case associated with a software application;
• retrieve a prompt template based on the query, wherein the prompt template comprises:
• natural language instructions related to providing a response to queries corresponding to the use case, and
• one or more placeholders associated with a placeholder comprising an indication of a location within a data source that contains data associated with the use case;
• retrieve, based on the placeholder, data associated with the user from the indicated location in the data source;
• populate the prompt template to produce a dynamically generated prompt by replacing the placeholder with the retrieved data;
• and
• provide the dynamically generated prompt and the query to a machine learning model that has been trained to generate content in response to an input prompt and an input query.