Abstract: Computer-implemented systems and methods including language models for explaining and resolving code errors. A computer-implemented method may include: receiving or accessing a log comprising an error message, the error message indicating an error in code; determining the error message from the log; determining a context associated with the error; generating a prompt for a large language model (“LLM”), the prompt comprising at least: the error message, and the context associated with the error; transmitting the prompt to the LLM; and receiving an output from the LLM in response to the prompt, the output comprising at least: an explanation of the error message, and a suggested fix for the error.

Abstract: Computer-implemented systems and methods including language models for explaining and resolving code errors. A computer-implemented method may include: receiving or accessing a log comprising an error message, the error message indicating an error in code; determining the error message from the log; determining a context associated with the error; generating a prompt for a large language model (“LLM”), the prompt comprising at least: the error message, and the context associated with the error; transmitting the prompt to the LLM; and receiving an output from the LLM in response to the prompt, the output comprising at least: an explanation of the error message, and a suggested fix for the error.

Abstract: Computer-implemented systems and methods are disclosed, including for remotely modifying a configuration file defining a computing environment configuration.

Abstract: Computer-implemented systems and methods including language models for explaining and resolving code errors. A computer-implemented method may include: receiving or accessing a log comprising an error message, the error message indicating an error in code; determining the error message from the log; determining a context associated with the error; generating a prompt for a large language model (“LLM”), the prompt comprising at least: the error message, and the context associated with the error; transmitting the prompt to the LLM; and receiving an output from the LLM in response to the prompt, the output comprising at least: an explanation of the error message, and a suggested fix for the error.

Abstract: An artificial intelligence system can be used to respond to natural language inputs (e.g., user submitted inputs) where the response involves a data processing workflow involving language models. The artificial intelligence system can use “profiles” associated with a user, role, cohort, and/or organization to bring additional operational context into user interactions within the artificial intelligence system.

Abstract: The present invention relates to an in vitro method for the diagnosis and treatment guidance of a bacterial infection in patients suffering from an acute ischemic or hemorrhagic stroke, comprising the determination of the level of Procalcitonin (PCT) or a fragment thereof having at least 12 amino acid residues in a sample of a bodily fluid from said patient and the correlation of the determined level to the diagnosis of a bacterial infection in said patient.

Abstract: The present invention relates to an in vitro method for the diagnosis and treatment guidance of a bacterial infection in patients suffering from an acute ischemic or hemorrhagic stroke, comprising the determination of the level of Procalcitonin (PCT) or a fragment thereof having at least 12 amino acid residues in a sample of a bodily fluid from said patient and the correlation of the determined level to the diagnosis of a bacterial infection in said patient.