Abstract: The present disclosure relates generally to systems and methods for updating an input prompt for a generative AI model (e.g., an LLM) based on feedback that is provided in connection with an output from the generative AI model that is unsatisfactory. For example, where a user indicated that an output from the generative AI model is incorrect, inaccurate, or is an otherwise unsatisfactory response to an input prompt, this disclosure describes models to facilitate generation of feedback hints and/or additional information that can be included within an updated prompt that, when provided as an input to the generative AI model, has an improved likelihood to return an output that is in-line with user expectations. Indeed, features of the systems and methods described herein provide a framework for improving outputs of generative AI models that are more accurate or otherwise responsive to the input prompts.

Abstract: Systems, methods and computer-readable memory devices are provided for greater efficiency in the configuration of a database cluster for performing a query workload. A database cluster configuration system is provided that includes a database cluster comprising one or more compute resources configured to perform database queries. A query workload comprising a plurality of queries is received. An initial workload-level configuration is applied. For each query of the query workload, a query-level configuration is generated using a query configuration model corresponding to each query in a contextual Bayesian optimization with centroid learning while also leveraging the query plan for each executing query for query characterization and including application of virtual operators. Query events are collected and used to update the corresponding query configuration model. The workload-level configuration is updated based on the query events and cached for use during a subsequent execution of the workload.