Large Language Model (LLM) Selection Using Artificial Intelligence (AI) System Networks

A method comprising:

• at a computing platform comprising at least one processor, a communication interface, and memory:
• training, for a first large language model (LLM) of a plurality of LLMs and using historical information for the plurality of LLMs and model network information, an LLM selection model, wherein training the LLM selection model configures the LLM selection model to select one of the plurality of LLMs for providing a response to a given input query;
• inputting, into the first LLM, an LLM prompt, wherein inputting the LLM prompt causes the first LLM to generate an LLM output by:
• identifying a first confidence level that a first output by the first LLM will be accurate,
• comparing the first confidence level that the first output will be accurate to a confidence threshold,
• based on identifying that the first confidence level that the first output will be accurate meets or exceeds the confidence threshold, generating, using the first LLM, the LLM output,
• based on identifying that the first confidence level that the first output will be accurate fails to meet the confidence threshold:
• identifying, using the LLM selection model, an alternative LLM of the plurality of LLMs, wherein a second confidence level associated with the alternative LLM producing the first output meets or exceeds the confidence threshold, and
• inputting the LLM prompt into the alternative LLM, wherein the alternative LLM produces the LLM output;
• and
• transmitting, to a user device associated with the LLM prompt, the LLM output and one or more commands directing the user device to display the LLM output, wherein sending the one or more commands directing the user device to display the LLM output causes the user device to display the LLM output.

One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, a communication interface, and memory, cause the computing platform to:

• train, for a first large language model (LLM) of a plurality of LLMs and using historical information for the plurality of LLMs and model network information, an LLM selection model, wherein training the LLM selection model configures the LLM selection model to select one of the plurality of LLMs for providing a response to a given input query;
• input, into the first LLM, an LLM prompt, wherein inputting the LLM prompt causes the first LLM to generate an LLM output by:
• identifying a first confidence level that a first output by the first LLM will be accurate,
• comparing the first confidence level that the first output will be accurate to a confidence threshold,
• based on identifying that the first confidence level that the first output will be accurate meets or exceeds the confidence threshold, generating, using the first LLM, the LLM output,
• based on identifying that the first confidence level that the first output will be accurate fails to meet the confidence threshold:
• identifying, using the LLM selection model, an alternative LLM of the plurality of LLMs, wherein a second confidence level associated with the alternative LLM producing the first output meets or exceeds the confidence threshold, and
• inputting the LLM prompt into the alternative LLM, wherein the alternative LLM produces the LLM output;
• and
• transmit, to a user device associated with the LLM prompt, the LLM output and one or more commands directing the user device to display the LLM output, wherein sending the one or more commands directing the user device to display the LLM output causes the user device to display the LLM output.