SYSTEMS AND METHODS OF LARGE LANGUAGE MODEL DRIVEN ORCHESTRATION OF TASK-SPECIFIC MACHINE LEARNING SOFTWARE AGENTS
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United States of America
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Issued Date -
Apr 10, 2025
app pub date -
Aug 1, 2024
filing date -
Aug 1, 2024
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Abstract
Systems and methods of the present disclosure may receive, from a user computing device, a user-provided data record query including a natural language request for information associated with one or more data sources. User persona attributes of the user may be determined, such as a user role or security parameters or both. Based on the user persona attributes a context query may be generated to obtain context attributes associated with the user-provided query. The natural language request and the context attributes are input into the model orchestration large language model (LLM) to output instructions to machine learning (ML) agents based on the context attributes. The ML agents output responses associated with the user-provided data record query based on the instructions, and the responses are input into the model orchestration LLM to output to the user computing device a natural language response based on the context attributes.
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