Abstract: Methods and systems are presented for providing a retrieval-augmented generation (RAG) framework that provides information associated with a particular domain to an artificial intelligence (AI). The RAG framework includes a query generation module that generates additional queries based on an original user query. A set of documents is retrieved from a corpus based on the user query and the additional queries. The RAG framework further includes an evaluation module for evaluating a relevancy of each retrieved document with respect to the original user query. Any documents determined to be irrelevant to the original user query may be eliminated, and the remaining documents along with the original user query are used to generate a prompt for the AI model, which in turn, generates a response to the user query.

Abstract: Methods and systems are presented for providing a retrieval-augmented generation (RAG) framework that provides information associated with a particular domain to an artificial intelligence (AI). The RAG framework includes a query generation module that generates additional queries based on an original user query. A set of documents is retrieved from a corpus based on the user query and the additional queries. The RAG framework further includes an evaluation module for evaluating a relevancy of each retrieved document with respect to the original user query. Any documents determined to be irrelevant to the original user query may be eliminated, and the remaining documents along with the original user query are used to generate a prompt for the AI model, which in turn, generates a response to the user query.

Abstract: Techniques are disclosed relating to training a prediction model using a pipeline of machine learning models. A system embeds values of records of at least two different data sources within a multi-dimensional embedding space. The system may calculate similarity scores for respective pairs of clusters within the multi-dimensional embedding space. Based on the similarity scores, the system identifies correlations between values of records from the two different data sources. Based on the identified correlations, the system generates matching features and inputs the matching features into a matching model. Based on output of the matching model for the matching features, the system combines similar records from the at least two different data sources, where the combining produces an enhanced data source. The system may then input the enhanced data source into the prediction model. The disclosed record matching techniques may advantageously provide customized matching for prediction models.