Methods for generating synthetic labels and devices thereof

A non-transitory computer readable medium having stored thereon instructions for detecting drift in models comprising executable code which when executed by one or more processors, causes the processors to:

• generate a prompt comprising input data;
• determine whether a prediction model is drifting by comparing a prediction label against a synthetic label for a match, wherein the prediction model generates the prediction label based on the input data, wherein the synthetic label is received from a large language model (LLM), and wherein the LLM is configured to generate the synthetic label based on the prompt;
• and
• in response to determining the prediction label and the synthetic label do not match, initiate a corrective action.

A network traffic manager apparatus, comprising memory comprising programmed instructions stored in the memory and one or more processors configured to be capable of executing the programmed instructions stored in the memory to:

• generate a prompt comprising input data;
• determine whether a prediction model is drifting by comparing a prediction label against a synthetic label for a match, wherein the prediction model generates the prediction label based on the input data, wherein the synthetic label is received from a large language model (LLM), and wherein the LLM is configured to generate the synthetic label based on the prompt;
• and
• in response to determining the prediction label and the synthetic label do not match, initiate a corrective action.

A network traffic management system, comprising one or more traffic management apparatuses, client devices, or server devices, the network traffic management system comprising memory comprising programmed instructions stored thereon and one or more processors configured to be capable of executing the programmed instructions to:

• generate a prompt comprising input data;
• determine whether a prediction model is drifting by comparing a prediction label against a synthetic label for a match, wherein the prediction model generates the prediction label based on the input data, wherein the synthetic label is received from a large language model (LLM), and wherein the LLM is configured to generate the synthetic label based on the prompt;
• and
• in response to determining the prediction label and the synthetic label do not match, initiate a corrective action.