The present technology relates to artificial intelligence assisted device troubleshooting. In an implementation, an interface service of a human machine interface application trains a machine learning model on the content of an embeddings database. The interface service then receives an input comprising a context of an automation system design. The interface service generates a prompt that includes an instruction for the ML model to identify an anomaly type associated with the context of the automation system design and to generate a solution that addresses the anomaly type. The interface service transmits the prompt to the ML model and receives a response from the ML model that includes the anomaly type and the requested solution. After receiving a response, the interface service may modify the automation system design based on the content of the response and surface a graphical user interface that includes the modified design.