Systems, methods, and apparatus for autotuning of retrieval augmented generation parameters

Abstract

Systems, apparatus, and methods for autotuning of retrieval augmented generation parameters are disclosed. Example instructions to perform the same include instructions to vectorize a dataset using retrieval augmented generation (RAG) parameters, the RAG parameters identified based on a domain of the dataset, wherein the RAG parameters control at least one of ingestion of data or retrieval of data, execute a machine learning model using the RAG parameters, evaluate a quality of an output of the machine learning model, after determination that the quality does not meet a quality threshold, update the RAG parameters and cause re-execution of the machine learning model using the updated RAG parameters, and after determination that the quality meets the quality threshold, store the RAG parameters in association with the domain of the dataset.

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