System and methods for one or more universal language models with data integration and output generation

A computer system for dynamically generating output through artificial intelligence (AI) driven data retrieval, processing, and contextual analysis of interconnected data sources, comprising:

• one or more processors of a computing system;
• and
• at least one non-transitory computer readable medium storing instructions which, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
• receiving a request from a user device, wherein the request includes a specified task or a query;
• utilizing a large language model (LLM) to process the request to determine a request purpose;
• transmitting data corresponding to the request purpose to a retrieval augmented generation (RAG) model;
• receiving relevant data associated with the request purpose from the RAG model, wherein the RAG model communicates with one or more agent-based AI systems to identify the relevant data from (i) the LLM, (ii) one or more small language models (SLMs), (iii) one or more dynamic data streams associated with one or more interconnected systems, or (iv) one or more generative software systems;
• transmitting the relevant data to a LLM data deconfliction model;
• utilizing the LLM data deconfliction model to processes the relevant data to reconcile, filter, or resolve conflicting or redundant information of the relevant data;
• generating the output based upon the relevant data, wherein the output is dynamically generated using the one or more generative software systems;
• and
• outputting the output to a user interface of the user device.

A non-transitory computer readable medium for dynamically generating output through artificial intelligence (AI) driven data retrieval, processing, and contextual analysis of interconnected data sources, the non-transitory computer readable medium storing instructions which, when executed by one or more processors of a computing system, cause the one or more processors to perform operations comprising:

• receiving a request from a user device, wherein the request includes a specified task or a query;
• utilizing a large language model (LLM) to process the request to determine a request purpose;
• transmitting data corresponding to the request purpose to a retrieval augmented generation (RAG) model;
• receiving relevant data associated with the request purpose from the RAG model, wherein the RAG model communicates with one or more agent-based AI systems to identify the relevant data from (i) the LLM, (ii) one or more small language models (SLMs), (iii) one or more dynamic data streams associated with one or more interconnected systems, or (iv) one or more generative software systems;
• transmitting the relevant data to a LLM data deconfliction model;
• utilizing the LLM data deconfliction model to processes the relevant data to reconcile, filter, or resolve conflicting or redundant information of the relevant data;
• generating the output based upon the relevant data, wherein the output is dynamically generated using the one or more generative software systems;
• and
• outputting the output to a user interface of the user device.