Machine learning model dynamic prompt engineering

Abstract

Techniques described herein involve dynamic prompt engineering using machine learning models. Embodiments include generating, using a machine learning model, a first output based on a sample data set. Embodiments include generating, using a fine-tuned machine learning model, a second output based on the sample data set, wherein the fine-tuned machine learning model is trained by iteratively adjusting parameters of the model based on comparing training inputs passed through multiple layers of the model to training outputs from the model. Embodiments include comparing the first output to the second output, wherein the comparing comprises identifying discrepancies between the first and the second output. Embodiments include automatically generating natural language instructions for providing to the machine learning model based on the comparing. Embodiments include performing an action using the machine learning model based on the natural language instructions and/or sending the natural language instructions to system components via a network communication protocol.