Large language model (llm) benchmarks for artificial intelligent (ai) agents use cases in a customer relationship management (crm) environment

One or more non-transitory computer-readable media storing instructions executable by one or more processors, wherein the instructions, when executed, cause the one or more processors to perform operations comprising:

• receiving at least one data set associated with a prompt for at least one agent use case;
• applying the prompt with the at least one data set to at least one LLM for the at least one agent use case to cause a response from an agent application;
• based on the response from the agent application, applying an algorithm to evaluate the response from the agent application based on at least one benchmark from a plurality of benchmarks related to the at least one LLM;
• and
• generating, based on the at least one benchmark related to the at least one LLM, a score related to a measure of a metric of an accuracy of the response from the agent application.

A method comprising:

• receiving at least one data set associated with a prompt for at least one agent use case;
• applying the prompt with the at least one data set to at least one LLM for the at least one agent use case to cause a response from an agent application;
• based on the response from the agent application, applying an algorithm to evaluate the response from the agent application based on at least one benchmark from a plurality of benchmarks related to the at least one LLM;
• and
• generating, based on the at least one benchmark related to the at least one LLM, a score related to a measure of a metric of an accuracy of the response from the agent application.