Personalization techniques for code generation platforms

A computer-implemented method to incrementally train an artificial intelligence model structured to generate personalized code units, the artificial intelligence model comprising a set of memory units and activation functions linking memory units in the set of memory units updatable to improve generated code unit accuracy, the method comprising:

• receiving a natural language query;
• determining a user persona based on contextual information regarding a user associated with the natural language query, the contextual information comprising at least one of:
• (i) user role information, or (ii) historical interaction pattern information;
• responsive to determining that at least a portion of the natural language query matches an ontology entry, accessing a corresponding code unit associated with the ontology entry, wherein the code unit is identified based on the determined user persona;
• responsive to determining that at least a portion of the natural language query does not match an ontology entry, applying the artificial intelligence model to generate the code unit by prompting the artificial intelligence model with the at least a portion of the natural language query and at least two of:
• (i) the user role information, (ii) the historical interaction pattern information, or (iii) the determined user persona;
• executing the code unit against a data source to generate a result set;
• and
• responsive to detecting a feedback item in connection with the result set or the code unit, incrementally training the artificial intelligence model using the detected feedback item, wherein incrementally training the artificial intelligence model comprises causing the artificial intelligence model to update, responsive to the detected feedback item, the set of memory units and the activation functions linking the memory units in the set of memory units.

A computing system comprising at least one data processor and at least one memory having instructions recorded thereon, wherein the instructions, when executed by the at least one data processor, cause the computing system to incrementally train an artificial intelligence model structured to generate personalized code units, the artificial intelligence model comprising a set of memory units and activation functions linking memory units in the set of memory units updatable to improve generated code unit accuracy, by performing operations to:

• receive a natural language query;
• determine a user persona based on contextual information regarding a user associated with the natural language query, the contextual information comprising at least one of:
• (i) user role information, or (ii) historical interaction pattern information;
• responsive to determining that at least a portion of the natural language query matches an ontology entry, access a corresponding code unit associated with the ontology entry, wherein the code unit is identified based on the determined user persona;
• responsive to determining that at least a portion of the natural language query does not match an ontology entry, apply the artificial intelligence model to generate the code unit by prompting the artificial intelligence model with the at least a portion of the natural language query and at least two of:
• (i) the user role information, (ii) the historical interaction pattern information, or (iii) the determined user persona;
• execute the code unit against a data source to generate a result set;
• and
• responsive to detecting a feedback item in connection with the result set or the code unit, incrementally train the artificial intelligence model using the detected feedback item, wherein incrementally training the artificial intelligence model comprises causing the artificial intelligence model to update, responsive to the detected feedback item, the set of memory units and the activation functions linking the memory units in the set of memory units.