System and method for determining execution performance of generative artificial intelligence (ai) models

A computer-implemented method for determining an execution performance of one or more generative artificial intelligence (AI) models, comprising:

• operating, by one or more servers through a workflow management subsystem, the one or more generative artificial intelligence (AI) models within a controlled workflow by providing input data to obtain first output data;
• obtaining, by the one or more servers through a data obtaining subsystem, at least one of:
• the input data, the first output data, and second output data from at least one of:
• the one or more generative artificial intelligence (AI) models, one or more users, and historic system data;
• retrieving, by the one or more servers through a context retrieval subsystem, context information from at least one of:
• the input data and the second output data stored in one or more databases, based on one or more queries provided to the one or more generative artificial intelligence (AI) models;
• generating, by the one or more servers through a score-generating subsystem, comprises:
• determining, by a correctness score generation subsystem, the correctness of the one or more generative artificial intelligence (AI) models based on performing a comparative analysis between the first output data against the second output data using at least one of:
• a pre-trained embedding model and a pre-trained X-encoder model for generating a correctness score;
• determining, by an output data pertinence determination subsystem, a pertinence of the first output data for the provided input data to the one or more generative artificial intelligence (AI) models using at least one of:
• a pre-trained zero-shot classifier and a clustering process, to generate a pertinence score;
• identifying, by an output data deviation detection subsystem, a deviation of the first output data generated by the one or more generative artificial intelligence (AI) models from the provided input data based on at least one of:
• the comparative analysis between the first output data and the second output data, and
• the comparative analysis between the first output data and the context information, for generating a similarity score;
• analyzing, by a profanity detection subsystem, the first output data to identify profanity data in the first output data using one or more machine learning models trained on pre-defined profanity data for generating a profanity score for the first output data;
• and
• determining, by a vulnerability detection subsystem, a susceptibility of the first output data based on comparing the first output data with one or more jailbreaking test cases for generating a vulnerability score;
• and
• executing, by the one or more servers through a performance detection subsystem, a comparative analysis between at least one of:
• the correctness score, the pertinence score, the similarity score, the profanity score, and the vulnerability score against respective threshold scores to generate a performance report to determine the execution performance of the one or more generative artificial intelligence (AI) models.

A non-transitory computer-readable storage medium storing computer-executable instructions that, when executed by one or more hardware processors, cause the one or more hardware processors to perform operations for determining an execution performance of one or more generative artificial intelligence (AI) models, the operations comprising:

• operating the one or more generative artificial intelligence (AI) models within a controlled workflow by providing input data to obtain first output data;
• obtaining at least one of:
• the input data, the first output data, and second output data from at least one of:
• the one or more generative artificial intelligence (AI) models, one or more users, and historic system data;
• retrieving context information from at least one of:
• the input data and the second output data stored in one or more databases, based on one or more queries provided to the one or more generative artificial intelligence (AI) models;
• determining the correctness of the one or more generative artificial intelligence (AI) models based on performing a comparative analysis between the first output data against the second output data using at least one of:
• a pre-trained embedding model and a pre-trained X-encoder model for generating a correctness score;
• determining a pertinence of the first output data for the provided input data to the one or more generative artificial intelligence (AI) models using at least one of:
• a pre-trained zero-shot classifier and a clustering process, to generate a pertinence score;
• identifying a deviation of the first output data generated by the one or more generative artificial intelligence (AI) models from the provided input data based on at least one of:
• the comparative analysis between the first output data and the second output data, and
• the comparative analysis between the first output data and the context information, for generating a similarity score;
• analyzing the first output data to identify profanity data in the first output data using one or more machine learning models trained on pre-defined profanity data for generating a profanity score for the first output data;
• determining a susceptibility of the first output data based on comparing the first output data with one or more jailbreaking test cases for generating a vulnerability score;
• and
• executing a comparative analysis between at least one of:
• the correctness score, the pertinence score, the similarity score, the profanity score, and the vulnerability score against respective threshold scores to generate a performance report to determine the execution performance of the one or more generative artificial intelligence (AI) models.