METHOD AND SYSTEM FOR ENHANCING LARGE LANGUAGE MODEL (LLM) UTILITY WITH CONTEXT-SPECIFIC TARGETED GENERATIVE MODELS

1 . A computerized method for creating a targeted generative model for generating text relevant to a specific context, the method comprising:

capturing data from an event while the event is in progress;

after conclusion of the event, isolating the captured data from external data not relevant to the event;

analyzing, using natural language processing, the isolated data to determine one or more categories for the isolated data;

extracting one or more facts from the data based on relevance to the event and accuracy;

concatenating the one or more facts into knowledge units;

grouping related knowledge units based on the one or more determined categories;

generating a targeted generative model, wherein the targeted generative model is specific to the event, and wherein the targeted generative model is in a secure, isolated environment;

training the targeted generative model using the knowledge units, wherein the targeted generative model is trained and operates within the secure, isolated environment, restricted to event-specific data, and while leveraging generative model infrastructure;

receiving a user query related to the event;

generating, using the targeted generative model, a response to the user query; and

outputting the response to the user query.

2 . The computerized method of claim 1 , wherein the event comprises a virtual meeting, video call, phone call, teleconference, video conference, webinar, or other virtual event.

3 . The computerized method of claim 1 , wherein the data comprises one or more of stakeholder join/leave events, chat, audio, video, or shared screens.

4 . The computerized method of claim 1 , wherein capturing the data comprises deploying digital notepads, transcription services, or artificial intelligence-driven context recognition software to capture details from the event.

5 . The computerized method of claim 1 , wherein the categories comprise topics, decisions, and action items from the event.

6 . The computerized method of claim 1 , wherein the extracted facts are meeting facts or experiences.

7 . The computerized method of claim 1 , wherein the targeted generative model is organized within an event-based database.

8 . The computerized method of claim 7 , wherein the user query is a natural language query.

9 . The computerized method of claim 1 , wherein the targeted generative model has enabled search functionality.

10 . The computerized method of claim 1 , wherein the user query requests specific details about events.

11 . The computerized method of claim 1 , wherein the targeted generative model is integrated within a meeting application.

12 . The computerized method of claim 1 , wherein data in the environment is restricted to designated secure zones such that there is no data transfer to or from publicly accessible generative model systems.

13 . The computerized method of claim 12 , wherein dedicated servers and virtual machines in the environment are physically and logically segregated from other network resources.

14 . The computerized method of claim 12 , wherein the environment applies an encryption safeguard to secure communication channels.

15 . The computerized method of claim 12 , wherein the environment applies a rigorous access control systems to maintain standard data integrity and confidentiality.

16 . A system comprising:

an electronic device comprising:

a meeting application configured to facilitate virtual communication between two or more people; and

a processor configured to:

capture data from an event in the meeting application while the event is in progress;

after conclusion of the event, isolating the captured data;

determining categories for the isolated data, wherein natural language processing is used to analyze the data;

extracting one or more facts from the data based on relevance and accuracy;

concatenating the one or more facts into knowledge units;

grouping related knowledge units;

generating a targeted generative model, wherein the targeted generative model is specific to the event, and wherein the targeted generative model is in a secure, isolated environment; and

training the targeted generative model using the knowledge units, wherein the targeted generative model is trained and operates within the secure, isolated environment, restricted to event-specific data, and while leveraging generative model infrastructure.

17 . The system of claim 16 , wherein the targeted generative model is integrated within the meeting application.

18 . The system of claim 16 , wherein data in the environment is restricted to designated secure zones such that there is no data transfer to or from publicly accessible generative model systems.

19 . The system of claim 16 , wherein the processor is further configured to:

receive a user query related to the event;

generate, using the targeted generative model, a response to the user query; and

output the response to the user query.

20 . A method for training a targeted generative model for generating text relevant to a specific context, the method comprising:

capturing data from an event;

after conclusion of the event, isolating the captured data;

determining categories for the isolated data;

extracting one or more facts from the data based on relevance and accuracy;

concatenating the one or more facts into knowledge units; and

training the targeted generative model using the knowledge units, wherein the targeted generative model is trained and operates within a secure, isolated environment, restricted to event-specific data, and while leveraging generative model infrastructure.