Adobe, Inc.

Manas Garg

Eunyee Koh

Shivakumar Vaithyanathan

Saayan Mitra

Anup Bandigadi Rao

Xiang Chen

Tong Yu

Uttaran Bhattacharya

Victor Soares Bursztyn

Kenneth George Russell

Akash Vivek Maharaj

Vaishnavi Muppala

Alexandru Ionut Hodorogea

Saurabh Tripathy

Ishita Dasgupta

Aleksander Pejcic

Some aspects relate to technologies for an artificial intelligent (AI) system that, among other things, enhances responses to concepts questions for an application with contextual usage insights. In accordance with some aspects, a user query is determined to comprise a concepts question regarding an application. Responsive to determining the user query comprises the concepts question, documentation regarding the application relevant to the user query is identified. A generative model generates text for a response to the concepts question using the documentation regarding the application. Additionally, a determination is made to add contextual usage insights to the response. Responsive to determining to add contextual usage insights to the response, usage data relevant to the user query and/or the response is retrieved. The generative model generates text for a final response using the response and the usage data, and the final response is provided to a user device for presentation.

Enhancing artificial intelligence responses with contextual usage insights

A method, apparatus, non-transitory computer readable medium, and system for document retrieval include obtaining a query and a document. A prompt generator generates a prompt for a reasoning model based on the query and the document. The reasoning model generates a reasoning result based on the prompt. In some cases, the reasoning result indicates that the document answers the query. A machine learning model provides the document in response to the query based on the reasoning result.

In-context and semantic-aware ensemble model for document retrieval

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Fremont, CA, United States of America

Manas Garg