System and method for transitioning from large language models to specialized machine learning models

Abstract

The present disclosure provides a computer-implemented method for reducing latency by transitioning from a Large Language Model (LLM) to a specialized Machine Learning model for performing operations within an application. The method includes receiving a user instruction specifying an operation, executing the operation based on LLM output, determining a performance metric for the LLM, identifying a specialized Machine Learning model, training the specialized model using stored data, deploying the specialized model within a software development kit, and redirecting subsequent instructions to the specialized model. The training process involves building a labeled dataset mapping user instruction to API calls or parameter sets and initializing the specialized model with pre-trained or random parameters. The method enables efficient handling of application-specific tasks by leveraging the initial capabilities of the LLM and subsequently transitioning to more focused, specialized models for improved performance.