XM INPUT PROCESSING SYSTEM FOR DEFINING PROMPTS FOR A LARGE LANGUAGE MODEL AND RECEIVING MODEL OUTPUTS

1 . A computer-implemented method comprising:

defining a prompt for a large language model that references dynamic experience data content;

receiving an experience data instance from a respondent device;

sending the experience data instance with the prompt to the large language model;

receiving, a first output from the large language model, the first output being generated based on the large language model analyzing the experience data instance according to the prompt; and

based on the first output, determining at least one action to perform with respect to the experience data instance.

2 . The computer-implemented method as recited in claim 1 , further comprising:

defining an additional prompt of the large language model that references the first output; and

based on the first output and the additional prompt, receiving a second output from the large language model to send to the respondent device.

3 . The computer-implemented method as recited in claim 1 , wherein the experience data instance comprises one of a survey response or digital journey data.

4 . The computer-implemented method as recited in claim 3 , wherein defining the prompt for the large language model comprises indicating a question type associated with the survey response.

5 . The computer-implemented method as recited in claim 1 , wherein defining the prompt for the large language model comprises indicating instructions to the large language model to determine responsive engagement factors that comprise at least one of a sentiment of the experience data instance, an intensity of the experience data instance, or an urgency level of the experience data instance.

6 . The computer-implemented method as recited in claim 1 , wherein the dynamic experience data content comprises experience data instances from a plurality of respondent devices.

7 . The computer-implemented method as recited in claim 1 , wherein defining the prompt further comprises:

defining experience data content categories for the large language model; and

receiving from the large language model a determined category of the experience data instance based on the defined experience data content categories.

8 . The computer-implemented method as recited in claim 1 , wherein determining the at least one action to perform comprises, based on the first output, determining an administrator client device to which to send the first output.

9 . The computer-implemented method as recited in claim 8 , further comprising:

determining, from the first output, responsive engagement factors and experience data content categories; and

selecting the administrator client device from a set of administrator devices based on the responsive engagement factors and the experience data content categories.

10 . The computer-implemented method as recited in claim 1 , further comprising:

identifying contextual data relating to the respondent device apart from the experience data instance;

providing the contextual data to the large language model with the experience data instance and the prompt; and

receiving the first output from the large language model, the first output generated based on the contextual data, the prompt, and the experience data instance.

11 . A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause a computer device to:

define a prompt for a large language model that references dynamic experience data content;

receive an experience data instance from a respondent device;

send the experience data instance with the prompt to the large language model;

receive, a first output from the large language model, the first output being generated based on the large language model analyzing the experience data instance according to the prompt; and

based on the first output, determine at least one action to perform with respect to the experience data instance.

12 . The non-transitory computer-readable medium of claim 11 , further comprising instructions that, when executed by the at least one processor, cause the computer device to:

define an additional prompt of the large language model that references the first output; and

based on the first output and the additional prompt, receive a second output from the large language model to send to the respondent device.

13 . The non-transitory computer-readable medium of claim 11 , further comprising instructions that, when executed by the at least one processor, cause the computer device to determine the at least one action to perform by determining an administrator client device from a set of administrator devices to send the first output.

14 . The non-transitory computer-readable medium of claim 11 , further comprising instructions that, when executed by the at least one processor, cause the computer device to define the prompt for the large language model by indicating instructions to the large language model to determine responsive engagement factors that comprise at least one of a sentiment of the experience data instance, an intensity of the experience data instance, or an urgency level of the experience data instance.

15 . The non-transitory computer-readable medium of claim 11 , further comprising instructions that, when executed by the at least one processor, cause the computer device to define the prompt by:

defining experience data content categories for the large language model; and

receiving from the large language model a determined category of the experience data instance based on the defined experience data content categories.

16 . The non-transitory computer-readable medium of claim 11 , further comprising instructions that, when executed by the at least one processor, cause the computer device to generate the first output by:

receiving a set of recommendations based on the experience data instance from the large language model; and

providing the first output that comprises the set of recommendations to an administrator client device.

17 . A system comprising:

at least one processor; and

at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to:

define a prompt for a large language model that references dynamic experience data content;

receive an experience data instance from a respondent device;

send the experience data instance with the prompt to the large language model;

receive, a first output from the large language model, the first output being generated based on the large language model analyzing the experience data instance according to the prompt; and

based on the first output, determine at least one action to perform with respect to the experience data instance.

18 . The system of claim 17 , further comprising instructions that, when executed by the at least one processor, cause the system to:

define an additional prompt of the large language model that references the first output; and

based on the first output and the additional prompt, receive a second output from the large language model to send to the respondent device.

19 . The system of claim 17 , further comprising instructions that, when executed by the at least one processor, cause the system to determine the at least one action to perform by determining an administrator client device from a set of administrator devices to send the first output.

20 . The system of claim 17 , further comprising instructions that, when executed by the at least one processor, cause the system to define the prompt for the large language model by:

determining responsive engagement factors that comprise at least one of a sentiment of the experience data instance, an intensity of the experience data instance, or an urgency level of the experience data instance; and

defining experience data content categories for the large language model.