AI-Powered Iterative Human-in-the-Loop Feedback System

A non-transitory computer-readable medium, encoded thereon computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform steps comprising:

• receiving, by an application performance automation (APA) system, a proposed modification to one or more source code files or configuration files of an application, the proposed modification generated by a large language model (LLM);
• presenting the proposed modification to a client device of a user;
• receiving, from the client device, a user comment in natural language in association with an acceptance or rejection of the proposed modification;
• extracting, from the user comment, one or more user preferences;
• storing the extracted user preferences in association with at least one of a user identifier, a code repository, or a category of performance inefficiency;
• detecting, during a future application performance optimization process, a candidate modification to a code file or configuration file;
• retrieving stored user preferences associated with at least one of the user identifier, the code repository, or the category of performance inefficiency;
• generating an updated LLM prompt based on the candidate modification and the retrieved user preferences;
• transmitting the updated LLM prompt to the LLM to cause the LLM to generate a revised modification that is tailored to the retrieved user preferences;
• and
• presenting the revised modification to the client device of the user.

A computing system, comprising:

• one or more processors;
• and
• a non-transitory computer-readable medium, encoded thereon computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform steps comprising:
• receiving, by an application performance automation (APA) system, a proposed modification to one or more source code files or configuration files of an application, the proposed modification generated by a large language model (LLM);
• presenting the proposed modification to a client device of a user;
• receiving, from the client device, a user comment in natural language in association with an acceptance or rejection of the proposed modification;
• extracting, from the user comment, one or more user preferences;
• storing the extracted user preferences in association with at least one of a user identifier, a code repository, or a category of performance inefficiency;
• detecting, during a future application performance optimization process, a candidate modification to a code file or configuration file;
• retrieving stored user preferences associated with at least one of the user identifier, the code repository, or the category of performance inefficiency;
• generating an updated LLM prompt based on the candidate modification and the retrieved user preferences;
• transmitting the updated LLM prompt to the LLM to cause the LLM to generate a revised modification that is tailored to the retrieved user preferences;
• and
• presenting the revised modification to the client device of the user.