Abstract: Embodiments of the disclosed technologies include receiving a first query including at least one first query term and configuring at least one prompt to cause a large language model to translate the at least one first query term into a set of functions that can be executed to obtain at least one second query term and generate and output a plan that is executable to create a modified version of the first query based on the at least one second query term. The plan is obtained by applying the large language model to the at least one prompt as configured. The plan is executed to determine the at least one second query term and create the modified version of the first query. The modified version of the first query is executed to provide, via the user interface, a response to the first query.

Abstract: In an example, a method is performed by sending query input and a first instruction to a generative machine learning model (GMLM). The first instruction is to cause the GMLM to generate and output a query input summary. The query input summary and a second instruction are sent to the GMLM. The second instruction is to cause the GMLM to provide query results using multiple different queries and the query input summary. The query results and a third instruction are to cause the GMLM to summarize a comparison of a query result with the query input summary. A query result evaluation summary is generated and output by the GMLM. A signal is received from a position within a presentation of the query result evaluation summary. The first instruction is updated to include the signal in the query input summary.

Abstract: An example sends query input and a first instruction to a generative machine learning model (GMLM). The first instruction is to cause the GMLM to generate and output a query input summary. The query input summary and a second instruction are sent to the GMLM. The second instruction is to cause the GMLM to provide query results using multiple different queries and the query input summary. The query results and a third instruction are to cause the GMLM to summarize a comparison of a query result with the query input summary. A query result evaluation summary is generated and output by the GMLM. A signal is received from a position within a presentation of the query result evaluation summary. The first instruction is updated to include the signal in the query input summary.

Abstract: An example provides a multi-agent system. Via an orchestrator agent, a query input is received. Via the orchestrator agent, the query input and a first instruction are provided to a generative machine learning model (GMLM). The first instruction is to cause the GMLM to determine queries using the query input. Via a query execution agent, the queries execute in parallel. Via the orchestrator agent, it is determined whether the queries are executing. Via a query evaluation agent, query results of execution of the queries and a second instruction are provided to the GMLM. The second instruction is to cause the GMLM to generate, for each query result, a query result summary. Via the orchestrator agent, the query result summaries are used to determine a subset of the query results for presentation via a device.

Abstract: Embodiments of the disclosed technologies include receiving a first query including at least one first query term and configuring at least one prompt to cause a large language model to translate the at least one first query term into a set of functions that can be executed to obtain at least one second query term and generate and output a plan that is executable to create a modified version of the first query based on the at least one second query term. The plan is obtained by applying the large language model to the at least one prompt as configured. The plan is executed to determine the at least one second query term and create the modified version of the first query. The modified version of the first query is executed to provide, via the user interface, a response to the first query.

Abstract: Embodiments of the disclosed technologies include receiving a first query including at least one first query term and configuring at least one prompt to cause a large language model to translate the at least one first query term into a set of functions that can be executed to obtain at least one second query term and generate and output a plan that is executable to create a modified version of the first query based on the at least one second query term. The plan is obtained by applying the large language model to the at least one prompt as configured. The plan is executed to determine the at least one second query term and create the modified version of the first query. The modified version of the first query is executed to provide, via the user interface, a response to the first query.

Abstract: Embodiments of the disclosed technologies include receiving a first query including at least one first query term and configuring at least one prompt to cause a large language model to translate the at least one first query term into a set of functions that can be executed to obtain at least one second query term and generate and output a plan that is executable to create a modified version of the first query based on the at least one second query term. The plan is obtained by applying the large language model to the at least one prompt as configured. The plan is executed to determine the at least one second query term and create the modified version of the first query. The modified version of the first query is executed to provide, via the user interface, a response to the first query.

Abstract: Disclosed herein are systems, methods, and non-transitory computer-readable media for providing user notifications based on a project context. The system may receive candidate attributes from candidate devices of a plurality of candidates and storing them in a candidate database, as well as user-entered attributes from a user device of a user. The system may then iteratively execute a number of operations that include performing a search for candidates in the candidate database by comparing project attributes with candidate attributes and providing user notification of newly-matched candidates that includes returning returned candidates that are matching candidates of the search results to the user based on the search.