Group action framework

1 . A computer-implemented method comprising:

obtaining an incident report;

assigning an incident type to the incident report;

generating a group of incident reports comprising the incident report and one or more other incident reports assigned to the incident type;

querying a database that stores associations between previously assigned incident types and corresponding responses previously generated by a large language model (LLM), each corresponding response specifying one or more actions to resolve the previously assigned incident type;

based on querying the database, determining that the database lacks a previously generated response by the LLM associated with the assigned incident type;

generating, using the LLM, a summary of the group of incident reports;

based on the summary and determining that the database lacks the previously generated response by the LLM associated with the assigned incident type, generating a prompt associated with the incident type; and

generating, using the LLM, a response to the incident report based on the prompt.

2 . The method of claim 1 , further comprising storing the response for the incident report at the database.

3 . The method of claim 1 , further comprising:

obtaining a second incident report; and

assigning a second incident type to the second incident report.

4 . The method of claim 3 , further comprising:

determining that the second incident type has been assigned to a particular prior incident report;

based on determining that the second incident type has been assigned to the particular prior incident report, identifying a second response stored at the database, the second response associated with the second incident type; and

retrieving the second response for the second incident report.

5 . The method of claim 4 , wherein the second response was previously generated by the LLM based on the particular prior incident report.

6 . The method of claim 1 , further comprising extracting data from the incident report.

7 . The method of claim 6 , further comprising determining one or more representative keywords for the incident report based on the extracted data.

8 . The method of claim 6 , further comprising determining one or more representative incident reports based on the extracted data.

9 . The method of claim 6 , further comprising:

generating a descriptive prompt based on the extracted data; and

generating, using the LLM, a description for the incident report based on the descriptive prompt.

10 . The method of claim 1 , further comprising generating a ranked list of incident reports for the group of incident reports based on a number of input tokens of each incident report within the group of incident reports.

11 . The method of claim 10 , the ranked list prioritizes a first incident report with a lower number of input tokens over a second incident report with a higher number of input tokens.

12 . The method of claim 11 , wherein generating the response to the incident report comprises selecting the incident report from the group of incident reports based on the ranked list.

13 . The method of claim 10 , further comprising:

for each incident report in the group of incident reports, generating, using the LLM, a respective output; and

generating, using the LLM, the incident report summary based on the respective output generated for each incident report.

14 . A system comprising:

data processing hardware; and

memory hardware in communication with the data processing hardware, the memory hardware storing instructions that when executed on the data processing hardware cause the data processing hardware to perform operations comprising:

obtaining an incident report;

assigning an incident type to the incident report;

generating a group of incident reports comprising the incident report and one or more other incident reports assigned to the incident type;

querying a database that stores associations between previously assigned incident types and corresponding responses previously generated by a large language model (LLM), each corresponding response specifying one or more actions to resolve the previously assigned incident type;

based on querying the database, determining that the database lacks a previously generated response by the LLM associated with the assigned incident type;

generating, using the LLM, a summary of the group of incident reports;

based on the summary and determining that the database lacks the stored previously generated response by the LLM associated with the assigned incident type, generating a prompt associated with the incident type; and

generating, using the LLM, a response to the incident report based on the prompt.

15 . The system of claim 14 , wherein the operations further comprise extracting data from the incident report.

16 . The system of claim 15 , wherein the operations further comprise determining one or more representative keywords for the incident report based on the extracted data.

17 . The system of claim 15 , wherein the operations further comprise determining one or more representative incident reports based on the extracted data.

18 . The system of claim 15 , wherein the operations further comprise:

generating a descriptive prompt based on the extracted data; and

generating, using the LLM, a description for the incident report based on the descriptive prompt.

19 . A non-transitory computer-readable medium having instructions that, when executed by data processing hardware, cause the data processing hardware to perform operations comprising:

obtaining an incident report;

assigning an incident type to the incident report;

generating a group of incident reports comprising the incident report and one or more other incident reports assigned to the incident type;

querying a database that stores associations between previously assigned incident types and corresponding responses previously generated by a large language model (LLM), each corresponding response specifying one or more actions to resolve the previously assigned incident type;

based on querying the database, determining that the database lacks a previously generated response by the LLM associated with the assigned incident type;

generating, using the LLM, a summary of the group of incident reports;

based on the summary and determining that the database lacks the previously generated response by the LLM associated with the assigned incident type, generating a prompt associated with the incident type; and

generating, using the LLM, a response to the incident report based on the prompt.