Abstract: A device may receive, from a user of a computing device and via an application, a user query. A device may generate a data package comprising the user query and user identity information. A device may transmit, via a company application programming interface (API) between the application and a generative response engine, the data package to the generative response engine. The generative response engine provides resources to process the user query based on entitlements associated with a user account. User anonymity and user entitlements can be fulfilled for the user experience when the user interacts with the generative response engine through the company API, even when the user is not logged into the generative response engine.

Abstract: There are many situations in which it is useful to identify similar entities or to quantify how similar one entity is to another. However, similarity is often subjective, making it challenging to determine the similarity of two entities computationally. In order to allow for determining the similarity of entities according to a customizable definition of similarity, a selection of features for determining similarity is received from a user device and a machine-learning model is trained to encode the values of the selected features for a particular entity as a representation. The representations of two different entities may be compared to determine the similarity of the two entities according to the selected features. The trained machine-learning model may, additionally or alternatively, be used for cluster analysis to identify similar groups of entities.

Abstract: Methods and systems for automatically prompting a LLM to generate a personalized text, such as a personalized textual description, in which portions of the text are customized based on user attributes. In various examples, responsive to a request for a textual description, a user record is retrieved for a user associated with the request and one or more user attributes are obtained based on the user record. In examples, a prompt to a large language model (LLM) for generating a user-specific textual description is generated, the prompt including the one or more user attributes to include in the generated user-specific textual description and a source text. The prompt is provided to the LLM to receive a generated user-specific textual description. The generated user-specific textual description is provided for display via a user device.