SYSTEM AND METHOD FOR PERSONALIZED AUTOMATED COACHING POWERED BY GENERATIVE AI

1 . A coaching simulator system comprising:

a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform operations which comprise:

receiving an interaction between a customer and an agent;

scoring the interaction using an evaluation form;

identifying a recurring improvement area for the agent based on the scored interaction and past scored interactions;

creating a prompt for a large language model (LLM) by populating a prompt template with a definition of the evaluation form, evaluation questions, a simulation objective, and a simulation example, wherein the definition of the evaluation form, the evaluation questions, the simulation objective and the simulation example are based on the recurring improvement area;

providing a framework to invoke the LLM using the created prompt, a model, and a plurality of hyperparameters;

starting a first coaching simulation scenario by invoking the LLM, via the framework, to present a first question to the agent;

receiving a first answer to the first question from the agent;

querying the LLM, via the framework, to analyze the first answer to the first question; and

querying the LLM, via the framework, to provide real-time feedback and a score for the agent based on the analyzed first answer to the first question.

2 . The coaching simulator system of claim 1 , wherein identifying a recurring improvement area for the agent comprises:

selecting past scored interactions with a score lower than a threshold score; and

identifying a most frequent scenario in the selected past scored interactions.

3 . The coaching simulator system of claim 1 , wherein the hyperparameters comprise a temperature hyperparameter and a top_p hyperparameter.

4 . The coaching simulator system of claim 1 , wherein providing a framework to invoke the LLM comprises maintaining a conversational memory of the first coaching simulation scenario.

5 . The coaching simulator system of claim 1 , wherein the operations further comprise:

determining that the score for the agent is below a predefined minimum score;

querying the LLM, by the framework, to ask the agent to answer the first question again;

receiving a second answer to the first question from the agent;

querying the LLM, by the framework, to analyze the second answer to the first question; and

querying the LLM, by the framework, to provide real-time feedback and a score for the agent based on the analyzed second answer to the first question.

6 . The coaching simulator system of claim 1 , wherein the operations further comprise:

determining that the score for the agent exceeds a predefined minimum score;

determining that there is a second question in the first coaching simulation scenario;

querying the LLM, by the framework, to present the second question to the agent;

receiving a first answer to the second question from the agent;

querying the LLM, by the framework, to analyze the first answer to the second question; and

querying the LLM, by the framework, to provide real-time feedback and a score for the agent based on the analyzed first answer to the second question.

7 . The coaching simulator system of claim 1 , wherein the operations further comprise:

determining that the score for the agent exceeds a predefined minimum score;

determining that there are no more questions in the first coaching simulation scenario;

querying the LLM, by the framework, to provide a summary report; and

confirming that the agent wishes to continue to a second coaching simulation scenario.

8 . The coaching simulator system of claim 1 , wherein the LLM comprises a generative pre-trained transformer (GPT).

9 . A method for simulating a coaching session, which comprises:

receiving an interaction between a customer and an agent;

scoring the interaction using an evaluation form;

identifying a recurring improvement area for the agent based on the scored interaction and past scored interactions;

creating a prompt for a large language model (LLM) by populating a prompt template with a definition of the evaluation form, evaluation questions, a simulation objective, and a simulation example, wherein the definition of the evaluation form, the evaluation questions, the simulation objective and the simulation example are based on the recurring improvement area;

providing a framework to invoke the LLM using the created prompt, a model, and a plurality of hyperparameters;

starting a first coaching simulation scenario by invoking the LLM, via the framework, to present a first question to the agent;

receiving a first answer to the first question from the agent;

querying the LLM, via the framework, to analyze the first answer to the first question; and

querying the LLM, via the framework, to provide real-time feedback and a score for the agent based on the analyzed first answer to the first question.

10 . The method of claim 9 , wherein identifying a recurring improvement area for the agent comprises:

selecting past scored interactions with a score lower than a threshold score; and

identifying a most frequent scenario in the selected past scored interactions.

11 . The method of claim 9 , wherein the hyperparameters comprise a temperature hyperparameter and a top_p hyperparameter.

12 . The method of claim 9 , wherein providing a framework to invoke the LLM comprises maintaining a conversational memory of the first coaching simulation scenario.

13 . The method of claim 9 , which further comprises:

determining that the score for the agent is below a predefined minimum score;

querying the LLM, by the framework, to ask the agent to answer the first question again;

receiving a second answer to the first question from the agent;

querying the LLM, by the framework, to analyze the second answer to the first question; and

querying the LLM, by the framework, to provide real-time feedback and a score for the agent based on the analyzed second answer to the first question.

14 . The method of claim 9 , which further comprises:

determining that the score for the agent exceeds a predefined minimum score;

determining that there is a second question in the first coaching simulation scenario;

querying the LLM, by the framework, to present the second question to the agent;

receiving a first answer to the second question from the agent;

querying the LLM, by the framework, to analyze the first answer to the second question; and

querying the LLM, by the framework, to provide real-time feedback and a score for the agent based on the analyzed first answer to the second question.

15 . The method of claim 9 , which further comprises:

determining that the score for the agent exceeds a predefined minimum score;

determining that there are no more questions in the first coaching simulation scenario;

querying the LLM, by the framework, to provide a summary report; and

confirming that the agent wishes to continue to a second coaching simulation scenario.

16 . A non-transitory computer-readable medium having stored thereon computer-readable instructions executable by a processor to perform operations which comprise:

receiving an interaction between a customer and an agent;

scoring the interaction using an evaluation form;

identifying a recurring improvement area for the agent based on the scored interaction and past scored interactions;

creating a prompt for a large language model (LLM) by populating a prompt template with a definition of the evaluation form, evaluation questions, a simulation objective, and a simulation example, wherein the definition of the evaluation form, the evaluation questions, the simulation objective and the simulation example are based on the recurring improvement area;

providing a framework to invoke the LLM using the created prompt, a model, and a plurality of hyperparameters;

starting a first coaching simulation scenario by invoking the LLM, via the framework, to present a first question to the agent;

receiving a first answer to the first question from the agent;

querying the LLM, via the framework, to analyze the first answer to the first question; and

querying the LLM, via the framework, to provide real-time feedback and a score for the agent based on the analyzed first answer to the first question.

17 . The non-transitory computer-readable medium of claim 16 , wherein identifying a recurring improvement area for the agent comprises:

selecting past scored interactions with a score lower than a threshold score; and

identifying a most frequent scenario in the selected past scored interactions.

18 . The non-transitory computer-readable medium of claim 16 , wherein the operations further comprise:

determining that the score for the agent is below a predefined minimum score;

querying the LLM, by the framework, to ask the agent to answer the first question again;

receiving a second answer to the first question from the agent;

querying the LLM, by the framework, to analyze the second answer to the first question; and

querying the LLM, by the framework, to provide real-time feedback and a score for the agent based on the analyzed second answer to the first question.

19 . The non-transitory computer-readable medium of claim 16 , wherein the operations further comprise:

determining that the score for the agent exceeds a predefined minimum score;

determining that there is a second question in the first coaching simulation scenario;

querying the LLM, by the framework, to present the second question to the agent;

receiving a first answer to the second question from the agent;

querying the LLM, by the framework, to analyze the first answer to the second question; and

querying the LLM, by the framework, to provide real-time feedback and a score for the agent based on the analyzed first answer to the second question.

20 . The non-transitory computer-readable medium of claim 16 , wherein the operations further comprise:

determining that the score for the agent exceeds a predefined minimum score;

determining that there are no more questions in the first coaching simulation scenario;

querying the LLM, by the framework, to provide a summary report; and

confirming that the agent wishes to continue to a second coaching simulation scenario.