While dashboards provide structured information on various metrics, they cannot answer specialized questions. For such questions, data scientists need to develop custom database queries based on natural language questions specified by users, which is costly and time-consuming. This disclosure describes a virtual data assistant that leverages large language models to automatically translate natural language queries to structured database queries and to translate structured responses into natural language answers. The models can be trained on pairs {natural language query, structured query} and {structured answer, natural language answer}. The virtual data assistant can be implemented as a chatbot that enables users to specify natural language queries. The LLMs are utilized in combination with a scripting language to automatically identify appropriate data sources and generate a database query; execute the query to obtain data responsive to the natural language query; and translate the answer to natural language for presentation to a requesting user. The virtual data assistant can enable cross-functional teams to quickly obtain answers without having to rely on data scientists and increase the productivity and speed of data driven decisions for the cross-functional teams.

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This work is licensed under a Creative Commons Attribution 4.0 License.